ORCID Profile
0000-0001-8371-8197
Current Organisation
University of Technology Sydney
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In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Neural, Evolutionary and Fuzzy Computation | Artificial Intelligence and Image Processing | Computer-Human Interaction | Adaptive Agents and Intelligent Robotics | Information Systems | Pattern Recognition and Data Mining | Decision Support and Group Support Systems |
Application Software Packages (excl. Computer Games) | Expanding Knowledge in the Information and Computing Sciences | Expanding Knowledge in Engineering | Computer Software and Services not elsewhere classified | Information Processing Services (incl. Data Entry and Capture)
Publisher: IEEE
Date: 08-2011
Publisher: IEEE
Date: 11-2019
Publisher: Wiley
Date: 22-02-2022
DOI: 10.1002/BRB3.2481
Abstract: Nurses represent the largest sector of the healthcare workforce, and it is established that they are faced with ongoing physical and mental demands that leave many continuously stressed. In turn, this chronic stress may affect cardiac autonomic activity, which can be non‐invasively evaluated using heart rate variability (HRV). The association between neurocognitive parameters during acute stress situations and HRV has not been previously explored in nurses compared to non‐nurses and such, our study aimed to assess these differences. Neurocognitive data were obtained using the Mini‐Mental State Examination and Cognistat psychometric questionnaires. ECG‐derived HRV parameters were acquired during the Trier Social Stress Test. Between‐group differences were found in domain‐specific cognitive performance for the similarities ( p = .03), and judgment ( p = .002) domains and in the following HRV parameters: SDNN baseline, ( p = .004), LF preparation ( p = .002), SDNN preparation ( p = .002), HF preparation ( p = .02), and TP preparation ( p = .003). Negative correlations were found between HF power and domain‐specific cognitive performance in nurses. In contrast, both negative and positive correlations were found between HRV and domain‐specific cognitive performance in the non‐nurse group. The current findings highlight the prospective use of autonomic HRV markers in relation to cognitive performance while building a relationship between autonomic dysfunction and cognition.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2000
DOI: 10.1109/91.890330
Publisher: ACM
Date: 08-05-2021
Publisher: American Scientific Publishers
Date: 02-2013
Publisher: Wiley
Date: 08-08-2012
DOI: 10.1111/J.1469-8986.2011.01270.X
Abstract: The present study investigated the brain dynamics accompanying spatial navigation based on distinct reference frames. Participants preferentially using an allocentric or an egocentric reference frame navigated through virtual tunnels and reported their homing direction at the end of each trial based on their spatial representation of the passage. Task-related electroencephalographic (EEG) dynamics were analyzed based on independent component analysis (ICA) and subsequent clustering of independent components. Parietal alpha desynchronization during encoding of spatial information predicted homing performance for participants using an egocentric reference frame. In contrast, retrosplenial and occipital alpha desynchronization during retrieval covaried with homing performance of participants using an allocentric reference frame. These results support the assumption of distinct neural networks underlying the computation of distinct reference frames and reveal a direct relationship of alpha modulation in parietal and retrosplenial areas with encoding and retrieval of spatial information for homing behavior.
Publisher: Springer Berlin Heidelberg
Date: 2003
Publisher: Springer Berlin Heidelberg
Date: 2003
Publisher: Elsevier BV
Date: 08-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2001
DOI: 10.1109/89.943339
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: IEEE
Date: 07-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-1998
DOI: 10.1109/72.661124
Abstract: This paper proposes Runge-Kutta neural networks (RKNNs) for identification of unknown dynamical systems described by ordinary differential equations (i.e., ordinary differential equation or ODE systems) with high accuracy. These networks are constructed according to the Runge-Kutta approximation method. The main attraction of the RKNNs is that they precisely estimate the changing rates of system states (i.e., the right-hand side of the ODE x =f(x)) directly in their subnetworks based on the space-domain interpolation within one s ling interval such that they can do long-term prediction of system state trajectories. We show theoretically the superior generalization and long-term prediction capability of the RKNNs over the normal neural networks. Two types of learning algorithms are investigated for the RKNNs, gradient-and nonlinear recursive least-squares-based algorithms. Convergence analysis of the learning algorithms is done theoretically. Computer simulations demonstrate the proved properties of the RKNNs.
Publisher: World Scientific Pub Co Pte Lt
Date: 21-02-2016
DOI: 10.1142/S0129065716500076
Abstract: Motion sickness (MS) is a common experience of travelers. To provide insights into brain dynamics associated with MS, this study recruited 19 subjects to participate in an electroencephalogram (EEG) experiment in a virtual-reality driving environment. When riding on consecutive winding roads, subjects experienced postural instability and sensory conflict between visual and vestibular stimuli. Meanwhile, subjects rated their level of MS on a six-point scale. Independent component analysis (ICA) was used to separate the filtered EEG signals into maximally temporally independent components (ICs). Then, reduced logarithmic spectra of ICs of interest, using principal component analysis, were decomposed by ICA again to find spectrally fixed and temporally independent modulators (IMs). Results demonstrated that a higher degree of MS accompanied increased activation of alpha ([Formula: see text]) and gamma ([Formula: see text]) IMs across remote-independent brain processes, covering motor, parietal and occipital areas. This co-modulatory spectral change in alpha and gamma bands revealed the neurophysiological demand to regulate conflicts among multi-modal sensory systems during MS.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2020
Publisher: Association for Computing Machinery (ACM)
Date: 31-01-2020
DOI: 10.1145/3344998
Abstract: Alzheimer’s disease is an incurable neurodegenerative disease primarily affecting the elderly population. Efficient automated techniques are needed for early diagnosis of Alzheimer’s. Many novel approaches are proposed by researchers for classification of Alzheimer’s disease. However, to develop more efficient learning techniques, better understanding of the work done on Alzheimer’s is needed. Here, we provide a review on 165 papers from 2005 to 2019, using various feature extraction and machine learning techniques. The machine learning techniques are surveyed under three main categories: support vector machine (SVM), artificial neural network (ANN), and deep learning (DL) and ensemble methods. We present a detailed review on these three approaches for Alzheimer’s with possible future directions.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Foundation of Computer Science
Date: 29-02-2012
DOI: 10.5120/4885-7195
Publisher: Wiley
Date: 06-10-2023
Publisher: Elsevier BV
Date: 09-2000
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2002
DOI: 10.1109/6979.994798
Publisher: MDPI AG
Date: 27-12-2023
DOI: 10.3390/APP13010302
Abstract: Freezing of gait (FOG) severely incapacitates the mobility of patients with advanced Parkinson’s disease (PD). An accurate prediction of the onset of FOG could improve the quality of life for PD patients. However, it is imperative to distinguish the possibility of the onset of FOG from that of voluntary stopping. Our previous work demonstrated the neurological differences between the transition to FOG and voluntary stopping using electroencephalogram (EEG) signals. We employed a timed up-and-go (TUG) task to elicit FOG in PD patients. Some of these TUG tasks had an additional voluntary stopping component, where participants stopped walking based on verbal instruction to “stop”. The performance of the convolutional neural network (CNN) in identifying the transition to FOG from normal walking and the transition to voluntary stopping was explored. To the best of our knowledge, this work is the first study to propose a deep learning method to distinguish the transition to FOG from the transition to voluntary stop in PD patients. The models, trained on the EEG data from 17 PD patients who manifested FOG episodes, considering a short two-second transition window for FOG occurrence or voluntary stopping, achieved close to 75% classification accuracy in distinguishing transition to FOG from the transition to voluntary stopping or normal walking. Our results represent an important step toward advanced EEG-based cueing systems for smart FOG intervention, excluding the potential confounding of voluntary stopping.
Publisher: arXiv
Date: 2017
Publisher: SAGE Publications
Date: 11-2012
DOI: 10.5772/52862
Abstract: Humans are the most important tracking objects in surveillance systems. However, human tracking is not enough to provide the required information for personalized recognition. In this paper, we present a novel and reliable framework for automatic age estimation based on computer vision. It exploits global face features based on the combination of Gabor wavelets and orthogonal locality preserving projections. In addition, the proposed system can extract face aging features automatically in real-time. This means that the proposed system has more potential in applications compared to other semi-automatic systems. The results obtained from this novel approach could provide clearer insight for operators in the field of age estimation to develop real-world applications.
Publisher: Elsevier BV
Date: 05-2021
Publisher: IEEE
Date: 2002
Publisher: IEEE
Date: 02-2020
Publisher: arXiv
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2019
Publisher: World Scientific Pub Co Pte Lt
Date: 12-1996
DOI: 10.1142/S0218488596000330
Abstract: This paper proposes a four-layered fuzzy language acquisition network (FLAN) for acquiring fuzzy language. It can catch the intended information from a sentence (command) spoken in natural language with fuzzy terms. The intended information includes a meaningful semantic action and the fuzzy linguistic information of that action (for ex le, the phrase “move forward” represents the meaningful semantic action and the phrase “very high speed” represents the linguistic information in the fuzzy command “Move forward in a very high speed.”). The proposed FLAN has two important features. First, we can make no restrictions whatever on the fuzzy language input which is used to specify the desired information, and the network requires no acoustic, prosodic, grammar and syntactic structure. Second, the linguistic information of an action is learned automatically and it is represented by fuzzy numbers based on α-level sets. A supervised learning scheme is proposed to train the FLAN on fuzzy training data. This learning scheme consists of the mutual-information (MI) supervised learning algorithm for learning meaningful semantic actions, and the fuzzy backpropagation (FBP) learning algorithm for learning linguistic information. An experimental system is constructed to illustrate the performance and applicability of the proposed FLAN.
Publisher: IEEE
Date: 2004
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Frontiers Media SA
Date: 23-10-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2022
Publisher: SAGE Publications
Date: 04-04-2012
Abstract: Optical imaging of changes in total hemoglobin concentration ( HbT), cerebral blood volume ( CBV), and hemoglobin oxygen saturation ( SO 2 ) provides a means to investigate brain hemodynamic regulation. However, high-resolution transcranial imaging remains challenging. In this study, we applied a novel functional photoacoustic microscopy technique to probe the responses of single cortical vessels to left forepaw electrical stimulation in mice with intact skulls. Functional changes in HbT, CBV, and SO 2 in the superior sagittal sinus and different-sized arterioles from the anterior cerebral artery system were bilaterally imaged with unambiguous 36 × 65- μm 2 spatial resolution. In addition, an early decrease of SO 2 in single blood vessels during activation (i.e., ‘the initial dip’) was observed. Our results indicate that the initial dip occurred specifically in small arterioles of activated regions but not in large veins. This technique complements other existing imaging approaches for the investigation of the hemodynamic responses in single cerebral blood vessels.
Publisher: Springer Berlin Heidelberg
Date: 2004
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2000
DOI: 10.1109/91.842151
Publisher: IEEE
Date: 2003
Publisher: IEEE
Date: 2007
Publisher: IEEE
Date: 10-2007
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2023
Publisher: Elsevier
Date: 1999
Publisher: IEEE
Date: 08-2010
Publisher: Elsevier BV
Date: 11-2017
Publisher: IEEE
Date: 2004
Publisher: American Scientific Publishers
Date: 04-03-2024
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2006
Publisher: IEEE
Date: 10-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: arXiv
Date: 2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2017
Publisher: IEEE
Date: 2004
Publisher: IEEE
Date: 2005
Publisher: World Scientific Pub Co Pte Lt
Date: 27-04-2016
DOI: 10.1142/S0129065716500180
Abstract: Research has indicated that fatigue is a critical factor in cognitive lapses because it negatively affects an in idual’s internal state, which is then manifested physiologically. This study explores neurophysiological changes, measured by electroencephalogram (EEG), due to fatigue. This study further demonstrates the feasibility of an online closed-loop EEG-based fatigue detection and mitigation system that detects physiological change and can thereby prevent fatigue-related cognitive lapses. More importantly, this work compares the efficacy of fatigue detection and mitigation between the EEG-based and a nonEEG-based random method. Twelve healthy subjects participated in a sustained-attention driving experiment. Each participant’s EEG signal was monitored continuously and a warning was delivered in real-time to participants once the EEG signature of fatigue was detected. Study results indicate suppression of the alpha- and theta-power of an occipital component and improved behavioral performance following a warning signal these findings are in line with those in previous studies. However, study results also showed reduced warning efficacy (i.e. increased response times (RTs) to lane deviations) accompanied by increased alpha-power due to the fluctuation of warnings over time. Furthermore, a comparison of EEG-based and nonEEG-based random approaches clearly demonstrated the necessity of adaptive fatigue-mitigation systems, based on a subject’s cognitive level, to deliver warnings. Analytical results clearly demonstrate and validate the efficacy of this online closed-loop EEG-based fatigue detection and mitigation mechanism to identify cognitive lapses that may lead to catastrophic incidents in countless operational environments.
Publisher: Elsevier BV
Date: 11-2021
Publisher: Association for Computing Machinery (ACM)
Date: 31-01-2020
DOI: 10.1145/3381919
Publisher: Frontiers Media SA
Date: 04-09-2014
Publisher: ACM
Date: 24-09-2021
Publisher: Elsevier BV
Date: 08-2010
DOI: 10.1016/J.NEUROIMAGE.2010.04.250
Abstract: This study investigates brain dynamics and behavioral changes in response to arousing auditory signals presented to in iduals experiencing momentary cognitive lapses during a sustained-attention task. Electroencephalographic (EEG) and behavioral data were simultaneously collected during virtual-reality (VR) based driving experiments, in which subjects were instructed to maintain their cruising position and compensate for randomly induced lane deviations using the steering wheel. 30-channel EEG data were analyzed by independent component analysis and the short-time Fourier transform. Across subjects and sessions, intermittent performance during drowsiness was accompanied by characteristic spectral augmentation or suppression in the alpha- and theta-band spectra of a bilateral occipital component, corresponding to brief periods of normal (wakeful) and hypnagogic (sleeping) awareness and behavior. Arousing auditory feedback was delivered to the subjects in half of the non-responded lane-deviation events, which immediately agitated subject's responses to the events. The improved behavioral performance was accompanied by concurrent spectral suppression in the theta- and alpha-bands of the bilateral occipital component. The effects of auditory feedback on spectral changes lasted 30s or longer. The results of this study demonstrate the amount of cognitive state information that can be extracted from noninvasively recorded EEG data and the feasibility of online assessment and rectification of brain networks exhibiting characteristic dynamic patterns in response to momentary cognitive challenges.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Hikari, Ltd.
Date: 2006
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2019
Publisher: IEEE
Date: 11-2201
Publisher: IEEE
Date: 04-0008
Publisher: S. Karger AG
Date: 20-07-2010
DOI: 10.1159/000230807
Abstract: Biomedical signal monitoring systems have rapidly advanced in recent years, propelled by significant advances in electronic and information technologies. Brain-computer interface (BCI) is one of the important research branches and has become a hot topic in the study of neural engineering, rehabilitation, and brain science. Traditionally, most BCI systems use bulky, wired laboratory-oriented sensing equipments to measure brain activity under well-controlled conditions within a confined space. Using bulky sensing equipments not only is uncomfortable and inconvenient for users, but also impedes their ability to perform routine tasks in daily operational environments. Furthermore, owing to large data volumes, signal processing of BCI systems is often performed off-line using high-end personal computers, hindering the applications of BCI in real-world environments. To be practical for routine use by unconstrained, freely-moving users, BCI systems must be noninvasive, nonintrusive, lightweight and capable of online signal processing. This work reviews recent online BCI systems, focusing especially on wearable, wireless and real-time systems.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Elsevier BV
Date: 09-2023
Publisher: World Scientific Pub Co Pte Lt
Date: 12-2005
DOI: 10.1142/S0218001405004411
Abstract: This paper presents a new algorithm for detection and compensation of backlight images. The proposed technique attacks the weakness of the conventional backlight image processing methods such as over-saturation, losing contrast and so on. The proposed algorithm consists of two operation phases: detection and compensation phases. In the detection phase, we use the spatial position characteristic and histogram of backlight image to obtain two image indices, which can determine the backlight degree of an image. Fuzzy logic is then used to integrate these two indices into a final backlight index determining the final backlight degree of an image precisely. Second, in the compensation phase, to solve the over-saturation problem that exists usually in conventional image compensation methods, we propose the adaptive compensation-curve scheme to compensate and enhance the brightness of backlight images. The luminance of a backlight image is adjusted according to the compensation curve, which is adapted dynamically according to the backlight degree indicated by the backlight index estimated in the detection phase. The performance of the proposed technique is tested on 100 backlight images covering various kinds of backlight conditions and degrees. The experimental and comparison results clearly show the superiority of the proposed technique.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2021
Publisher: IEEE
Date: 2004
Publisher: IEEE
Date: 2004
Publisher: arXiv
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2000
DOI: 10.1109/89.876300
Publisher: Springer International Publishing
Date: 2019
Publisher: IEEE
Date: 04-11-2020
Publisher: IEEE
Date: 11-07-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2022
Publisher: Springer Science and Business Media LLC
Date: 17-02-2016
DOI: 10.1038/SREP21353
Abstract: Fluctuations in attention behind the wheel poses a significant risk for driver safety. During transient periods of inattention, drivers may shift their attention towards internally-directed thoughts or feelings at the expense of staying focused on the road. This study examined whether increasing task difficulty by manipulating involved sensory modalities as the driver detected the lane-departure in a simulated driving task would promote a shift of brain activity between different modes of processing, reflected by brain network dynamics on electroencephalographic sources. Results showed that depriving the driver of salient sensory information imposes a relatively more perceptually-demanding task, leading to a stronger activation in the task-positive network. When the vehicle motion feedback is available, the drivers may rely on vehicle motion to perceive the perturbations, which frees attentional capacity and tends to activate the default mode network. Such brain network dynamics could have major implications for understanding fluctuations in driver attention and designing advance driver assistance systems.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2022
Publisher: IEEE
Date: 2000
Publisher: Elsevier BV
Date: 12-2011
Publisher: IEEE
Date: 2005
Publisher: arXiv
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Taiwanese Society of Biomedical Engineering
Date: 2010
DOI: 10.5405/JMBE.30.4.07
Publisher: Frontiers Media SA
Date: 03-2018
Publisher: IEEE
Date: 2005
Publisher: IEEE
Date: 07-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: IEEE
Date: 11-2010
DOI: 10.1109/TAAI.2010.46
Publisher: IOP Publishing
Date: 12-2022
Abstract: Objective. Error-related potential (ErrP)-based brain–computer interfaces (BCIs) have received a considerable amount of attention in the human–robot interaction community. In contrast to traditional BCI, which requires continuous and explicit commands from an operator, ErrP-based BCI leverages the ErrP, which is evoked when an operator observes unexpected behaviours from the robot counterpart. This paper proposes a novel shared autonomy model for ErrP-based human–robot interaction. Approach. We incorporate ErrP information provided by a BCI as useful observations for an agent and formulate the shared autonomy problem as a partially observable Markov decision process. A recurrent neural network-based actor-critic model is used to address the uncertainty in the ErrP signal. We evaluate the proposed framework in a simulated human-in-the-loop robot navigation task with both simulated users and real users. Main results. The results show that the proposed ErrP-based shared autonomy model enables an autonomous robot to complete navigation tasks more efficiently. In a simulation with 70% ErrP accuracy, agents completed the task 14.1% faster than in the no ErrP condition, while with real users, agents completed the navigation task 14.9% faster. Significance. The evaluation results confirmed that the shared autonomy via deep recurrent reinforcement learning is an effective way to deal with uncertain human feedback in a complex human–robot interaction task.
Publisher: Elsevier BV
Date: 11-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2000
DOI: 10.1109/8.876331
Publisher: arXiv
Date: 2017
Publisher: SPIE
Date: 10-02-2011
DOI: 10.1117/12.874449
Publisher: IEEE
Date: 10-2021
Publisher: MIT Press - Journals
Date: 13-05-2021
DOI: 10.1162/NECO_A_01382
Abstract: Driver mental fatigue leads to thousands of traffic accidents. The increasing quality and availability of low-cost electroencephalogram (EEG) systems offer possibilities for practical fatigue monitoring. However, non-data-driven methods, designed for practical, complex situations, usually rely on handcrafted data statistics of EEG signals. To reduce human involvement, we introduce a data-driven methodology for online mental fatigue detection: self-weight ordinal regression (SWORE). Reaction time (RT), referring to the length of time people take to react to an emergency, is widely considered an objective behavioral measure for mental fatigue state. Since regression methods are sensitive to extreme RTs, we propose an indirect RT estimation based on preferences to explore the relationship between EEG and RT, which generalizes to any scenario when an objective fatigue indicator is available. In particular, SWORE evaluates the noisy EEG signals from multiple channels in terms of two states: shaking state and steady state. Modeling the shaking state can discriminate the reliable channels from the uninformative ones, while modeling the steady state can suppress the task-nonrelevant fluctuation within each channel. In addition, an online generalized Bayesian moment matching (online GBMM) algorithm is proposed to online-calibrate SWORE efficiently per participant. Experimental results with 40 participants show that SWORE can maximally achieve consistent with RT, demonstrating the feasibility and adaptability of our proposed framework in practical mental fatigue estimation.
Publisher: Elsevier BV
Date: 10-2015
DOI: 10.1016/J.NEUROIMAGE.2015.07.009
Abstract: Studies on spatial navigation reliably demonstrate that the retrosplenial complex (RSC) plays a pivotal role for allocentric spatial information processing by transforming egocentric and allocentric spatial information into the respective other spatial reference frame (SRF). While more and more imaging studies investigate the role of the RSC in spatial tasks, high temporal resolution measures such as electroencephalography (EEG) are missing. To investigate the function of the RSC in spatial navigation with high temporal resolution we used EEG to analyze spectral perturbations during navigation based on allocentric and egocentric SRF. Participants performed a path integration task in a clearly structured virtual environment providing allothetic information. Continuous EEG recordings were decomposed by independent component analysis (ICA) with subsequent source reconstruction of independent time source series using equivalent dipole modeling. Time-frequency transformation was used to investigate reference frame-specific orientation processes during navigation as compared to a control condition with identical visual input but no orientation task. Our results demonstrate that navigation based on an egocentric reference frame recruited a network including the parietal, motor, and occipital cortices with dominant perturbations in the alpha band and theta modulation in frontal cortex. Allocentric navigation was accompanied by performance-related desynchronization of the 8-13 Hz frequency band and synchronization in the 12-14 Hz band in the RSC. The results support the claim that the retrosplenial complex is central to translating egocentric spatial information into allocentric reference frames. Modulations in different frequencies with different time courses in the RSC further provide first evidence of two distinct neural processes reflecting translation of spatial information based on distinct reference frames and the computation of heading changes.
Publisher: MDPI AG
Date: 12-09-2021
DOI: 10.3390/S21186104
Abstract: Subgroup label ranking aims to rank groups of labels using a single ranking model, is a new problem faced in preference learning. This paper introduces the Subgroup Preference Neural Network (SGPNN) that combines multiple networks have different activation function, learning rate, and output layer into one artificial neural network (ANN) to discover the hidden relation between the subgroups’ multi-labels. The SGPNN is a feedforward (FF), partially connected network that has a single middle layer and uses stairstep (SS) multi-valued activation function to enhance the prediction’s probability and accelerate the ranking convergence. The novel structure of the proposed SGPNN consists of a multi-activation function neuron (MAFN) in the middle layer to rank each subgroup independently. The SGPNN uses gradient ascent to maximize the Spearman ranking correlation between the groups of labels. Each label is represented by an output neuron that has a single SS function. The proposed SGPNN using conjoint dataset outperforms the other label ranking methods which uses each dataset in idually. The proposed SGPNN achieves an average accuracy of 91.4% using the conjoint dataset compared to supervised clustering, decision tree, multilayer perceptron label ranking and label ranking forests that achieve an average accuracy of 60%, 84.8%, 69.2% and 73%, respectively, using the in idual dataset.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2019
Publisher: Elsevier BV
Date: 2020
Publisher: Institute of Electronics, Information and Communications Engineers (IEICE)
Date: 08-2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2021
Publisher: Elsevier BV
Date: 11-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2007
Publisher: IEEE
Date: 06-2012
Publisher: IEEE
Date: 08-2012
Publisher: IEEE
Date: 05-2012
Publisher: Springer London
Date: 2005
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Elsevier BV
Date: 06-2012
Publisher: IEEE
Date: 11-2016
Publisher: IEEE
Date: 09-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2000
DOI: 10.1109/72.822519
Abstract: A physical modeling method for electronic music synthesis of plucked-string tones by using recurrent networks is proposed. A scattering recurrent network (SRN) which is used to analyze string dynamics is built based on the physics of acoustic strings. The measured vibration of a plucked string is employed as the training data for the supervised learning of the SRN. After the network is well trained, it can be regarded as the virtual model for the measured string and used to generate tones which can be very close to those generated by its acoustic counterpart. The "virtual string" corresponding to the SRN can respond to different "plucks" just like a real string, which is impossible using traditional synthesis techniques such as frequency modulation and wavetable. The simulation of modeling a cello "A"-string demonstrates some encouraging results of the new music synthesis technique. Some aspects of modeling and synthesis procedures are also discussed.
Publisher: Elsevier BV
Date: 03-2005
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: MDPI AG
Date: 25-11-2019
DOI: 10.3390/APP9235078
Abstract: As a major cause of vehicle accidents, the prevention of drowsy driving has received increasing public attention. Precisely identifying the drowsy state of drivers is difficult since it is an ambiguous event that does not occur at a single point in time. In this paper, we use an electroencephalography (EEG) image-based method to estimate the drowsiness state of drivers. The driver’s EEG measurement is transformed into an RGB image that contains the spatial knowledge of the EEG. Moreover, for considering the temporal behavior of the data, we generate these images using the EEG data over a sequence of time points. The generated EEG images are passed into a convolutional neural network (CNN) to perform the prediction task. In the experiment, the proposed method is compared with an EEG image generated from a single data time point, and the results indicate that the approach of combining EEG images in multiple time points is able to improve the performance for drowsiness prediction.
Publisher: IEEE
Date: 05-2019
Publisher: IEEE
Date: 07-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2021
Publisher: IEEE
Date: 11-2019
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Springer International Publishing
Date: 2023
Publisher: IEEE
Date: 08-2012
Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
Date: 18-05-2021
DOI: 10.1609/AAAI.V35I18.17871
Abstract: Imparting biological realism during the learning process is gaining attention towards producing computationally efficient algorithms without compromising the performance. Feedback alignment and mirror neuron concept are two such approaches where the feedback weight remains static in the former and update via Hebbian learning in the later. Though these approaches have proven to work efficiently for supervised learning, it remained unknown if the same can be applicable to reinforcement learning applications. Therefore, this study introduces RHebb-DFA where the reward-based Hebbian learning is used to update feedback weights in direct feedback alignment mode. This approach is validated on various Atari games and obtained equivalent performance in comparison with DDQN.
Publisher: IEEE
Date: 11-10-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2009
Publisher: MDPI AG
Date: 09-10-2020
DOI: 10.3390/BDCC4040027
Abstract: At present, traditional visual-based surveillance systems are becoming impractical, inefficient, and time-consuming. Automation-based surveillance systems appeared to overcome these limitations. However, the automatic systems have some challenges such as occlusion and retaining images smoothly and continuously. This research proposes a weighted res ling particle filter approach for human tracking to handle these challenges. The primary functions of the proposed system are human detection, human monitoring, and camera control. We used the codebook matching algorithm to define the human region as a target and track it, and we used the practical filter algorithm to follow and extract the target information. Consequently, the obtained information was used to configure the camera control. The experiments were tested in various environments to prove the stability and performance of the proposed system based on the active camera.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2023
Publisher: Elsevier BV
Date: 2018
Publisher: Springer Science and Business Media LLC
Date: 05-05-2022
DOI: 10.1007/S00521-022-07234-0
Abstract: In the financial market, the stock price prediction is a challenging task which is influenced by many factors. These factors include economic change, politics and global events that are usually recorded in text format, such as the daily news. Therefore, we assume that real-world text information can be used to forecast stock market activity. However, only a few works considered both text and numerical information to predict or analyse stock trends. These works used preprocessed text features as the model inputs therefore, latent information in text may be lost because the relationships between the text and stock price are not considered. In this paper, we propose a fusion network, i.e. a spatial-temporal attention-based convolutional network (STACN) that can leverage the advantages of an attention mechanism, a convolutional neural network and long short-term memory to extract text and numerical information for stock price prediction. Benefiting from the utilisation of an attention mechanism, reliable text features that are highly relevant to stock value can be extracted, which improves the overall model performance. The experimental results on real-world stock data demonstrate that our STACN model and training scheme can handle both text and numerical data and achieve high accuracy on stock regression tasks. The STACN is compared with CNNs and LSTMs with different settings, e.g. a CNN with only stock data, a CNN with only news titles and LSTMs with only stock data. CNNs considering only stock data and news titles have mean squared errors of 28.3935 and 0.1814, respectively. The accuracy of LSTMs is 0.0763. The STACN can achieve an accuracy of 0.0304, outperforming CNNs and LSTMs in stock regression tasks.
Publisher: IEEE
Date: 07-2012
Publisher: IEEE
Date: 06-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2021
Publisher: Hindawi Limited
Date: 2015
DOI: 10.1155/2015/725674
Abstract: A novel bioinspired control strategy design is proposed for generalized synchronization of nonlinear chaotic systems, combining the bioinspired stability theory, fuzzy modeling, and a novel, simple-form Lyapunov control function design of derived high efficient, heuristic and bioinspired controllers. Three main contributions are concluded: (1) apply the bioinspired stability theory to further analyze the stability of fuzzy error systems the high performance of controllers has been shown in previous study by Li and Ge 2009, (2) a new Lyapunov control function based on bioinspired stability theory is designed to achieve synchronization without using traditional LMI method, which is a simple linear homogeneous function of states and the process of designing controller to synchronize two fuzzy chaotic systems becomes much simpler, and (3) three different situations of synchronization are proposed classical master and slave Lorenz systems, slave Chen’s system, and Rossler’s system as functional system are illustrated to further show the effectiveness and feasibility of our novel strategy. The simulation results show that our novel control strategy can be applied to different and complicated control situations with high effectiveness.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2023
Publisher: IEEE
Date: 10-2006
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2016
Publisher: SPIE
Date: 13-04-2009
DOI: 10.1117/12.822610
Publisher: IEEE
Date: 29-05-2023
Publisher: ACM
Date: 08-05-2021
Publisher: IEEE
Date: 06-2019
Publisher: IEEE
Date: 2005
Publisher: IEEE
Date: 2005
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2003
Publisher: Elsevier BV
Date: 08-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2020
Publisher: Springer Science and Business Media LLC
Date: 18-02-2011
Publisher: Elsevier BV
Date: 09-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: IEEE
Date: 06-2011
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: IEEE
Date: 06-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Wiley
Date: 30-09-2019
DOI: 10.1002/BRB3.1379
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2014
Publisher: Wiley
Date: 02-12-2008
DOI: 10.1002/CTA.557
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2014
Publisher: Springer Science and Business Media LLC
Date: 30-05-2012
Publisher: Elsevier BV
Date: 08-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2022
Publisher: MDPI AG
Date: 17-09-2021
DOI: 10.3390/BIOS11090343
Abstract: In the assistive research area, human–computer interface (HCI) technology is used to help people with disabilities by conveying their intentions and thoughts to the outside world. Many HCI systems based on eye movement have been proposed to assist people with disabilities. However, due to the complexity of the necessary algorithms and the difficulty of hardware implementation, there are few general-purpose designs that consider practicality and stability in real life. Therefore, to solve these limitations and problems, an HCI system based on electrooculography (EOG) is proposed in this study. The proposed classification algorithm provides eye-state detection, including the fixation, saccade, and blinking states. Moreover, this algorithm can distinguish among ten kinds of saccade movements (i.e., up, down, left, right, farther left, farther right, up-left, down-left, up-right, and down-right). In addition, we developed an HCI system based on an eye-movement classification algorithm. This system provides an eye-dialing interface that can be used to improve the lives of people with disabilities. The results illustrate the good performance of the proposed classification algorithm. Moreover, the EOG-based system, which can detect ten different eye-movement features, can be utilized in real-life applications.
Publisher: IEEE
Date: 11-2008
Publisher: Elsevier BV
Date: 06-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2000
DOI: 10.1109/89.861373
Publisher: Research India Publications
Date: 2005
Publisher: IEEE
Date: 1991
Publisher: American Scientific Publishers
Date: 06-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2004
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2005
Publisher: Elsevier BV
Date: 07-2018
DOI: 10.1016/J.NEUROIMAGE.2018.03.032
Abstract: Inter- and intra-subject variability pose a major challenge to decoding human brain activity in brain-computer interfaces (BCIs) based on non-invasive electroencephalogram (EEG). Conventionally, a time-consuming and laborious training procedure is performed on each new user to collect sufficient in idualized data, hindering the applications of BCIs on monitoring brain states (e.g. drowsiness) in real-world settings. This study proposes applying hierarchical clustering to assess the inter- and intra-subject variability within a large-scale dataset of EEG collected in a simulated driving task, and validates the feasibility of transferring EEG-based drowsiness-detection models across subjects. A subject-transfer framework is thus developed for detecting drowsiness based on a large-scale model pool from other subjects and a small amount of alert baseline calibration data from a new user. The model pool ensures the availability of positive model transferring, whereas the alert baseline data serve as a selector of decoding models in the pool. Compared with the conventional within-subject approach, the proposed framework remarkably reduced the required calibration time for a new user by 90% (18.00 min-1.72 ± 0.36 min) without compromising performance (p = 0.0910) when sufficient existing data are available. These findings suggest a practical pathway toward plug-and-play drowsiness detection and can ignite numerous real-world BCI applications.
Publisher: Springer Science and Business Media LLC
Date: 25-04-2022
DOI: 10.1038/S41598-022-10855-Z
Abstract: To reduce the decline of spatial cognitive skills caused by the increasing use of automated GPS navigation, the virtual global landmark (VGL) system is proposed to help people naturally improve their sense of direction. Designed to accompany a heads-up navigation system, VGL system constantly displays silhouette of global landmarks in the navigator’s vision as a notable frame of reference. This study exams how VGL system impacts incidental spatial learning, i.e., subconscious spatial knowledge acquisition. We asked 55 participants to explore a virtual environment and then draw a map of what they had explored while capturing electroencephalogram (EEG) signals and eye activity. The results suggest that, with the VGL system, participants paid more attention during exploration and performed significantly better at the map drawing task—a result that indicates substantially improved incidental spatial learning. This finding might kickstart a redesigning navigation aids, to teach users to learn a route rather than simply showing them the way.
Publisher: American Scientific Publishers
Date: 06-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2008
Publisher: IEEE
Date: 2004
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-1997
DOI: 10.1109/3477.604107
Abstract: A new kind of nonlinear adaptive filter, the adaptive neural fuzzy filter (ANFF), based upon a neural network's learning ability and fuzzy if-then rule structure, is proposed in this paper. The ANFF is inherently a feedforward multilayered connectionist network which can learn by itself according to numerical training data or expert knowledge represented by fuzzy if-then rules. The adaptation here includes the construction of fuzzy if-then rules (structure learning), and the tuning of the free parameters of membership functions (parameter learning). In the structure learning phase, fuzzy rules are found based on the matching of input-output clusters. In the parameter learning phase, a backpropagation-like adaptation algorithm is developed to minimize the output error. There are no hidden nodes (i.e., no membership functions and fuzzy rules) initially, and both the structure learning and parameter learning are performed concurrently as the adaptation proceeds. However, if some linguistic information about the design of the filter is available, such knowledge can be put into the ANFF to form an initial structure with hidden nodes. Two major advantages of the ANFF can thus be seen: 1) a priori knowledge can be incorporated into the ANFF which makes the fusion of numerical data and linguistic information in the filter possible and 2) no predetermination, like the number of hidden nodes, must be given, since the ANFF can find its optimal structure and parameters automatically.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2003
Publisher: Springer Science and Business Media LLC
Date: 02-11-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: World Scientific Pub Co Pte Lt
Date: 02-2005
DOI: 10.1142/S0129065705000116
Abstract: Over one-third of protein structures contain metal ions, which are the necessary elements in life systems. Traditionally, structural biologists were used to investigate properties of metalloproteins (proteins which bind with metal ions) by physical means and interpreting the function formation and reaction mechanism of enzyme by their structures and observations from experiments in vitro. Most of proteins have primary structures (amino acid sequence information) only however, the 3-dimension structures are not always available. In this paper, a direct analysis method is proposed to predict the protein metal-binding amino acid residues from its sequence information only by neural networks with sliding window-based feature extraction and biological feature encoding techniques. In four major bulk elements (Calcium, Potassium, Magnesium, and Sodium), the metal-binding residues are identified by the proposed method with higher than 90% sensitivity and very good accuracy under 5-fold cross validation. With such promising results, it can be extended and used as a powerful methodology for metal-binding characterization from rapidly increasing protein sequences in the future.
Publisher: IEEE
Date: 2005
Publisher: Springer International Publishing
Date: 2017
Publisher: Frontiers Media SA
Date: 07-08-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2014
Publisher: American Scientific Publishers
Date: 06-2012
Publisher: IEEE
Date: 07-2013
Publisher: JMIR Publications Inc.
Date: 23-07-2020
Abstract: hronic pain is a global health problem, affecting around 1 in 5 in iduals in the general population. The understanding of the key role of functional brain alterations in the generation of chronic pain has led researchers to focus on pain treatments that target brain activity. Electroencephalographic neurofeedback attempts to modulate the power of maladaptive electroencephalography frequency powers to decrease chronic pain. Although several studies have provided promising evidence, the effect of electroencephalographic neurofeedback on chronic pain is uncertain. his systematic review aims to synthesize the evidence from randomized controlled trials to evaluate the analgesic effect of electroencephalographic neurofeedback. In addition, we will synthesize the findings of nonrandomized studies in a narrative review. e will apply the search strategy in 5 electronic databases (Cochrane Central Register of Controlled Trials, MEDLINE, EMBASE, PsycInfo, and CINAHL) for published studies and in clinical trial registries for completed unpublished studies. We will include studies that used electroencephalographic neurofeedback as an intervention for people with chronic pain. Risk-of-bias tools will be used to assess methodological quality of the included studies. We will include randomized controlled trials if they have compared electroencephalographic neurofeedback with any other intervention or placebo control. The data from randomized controlled trials will be aggregated to perform a meta-analysis for quantitative synthesis. The primary outcome measure is pain intensity assessed by self-report scales. Secondary outcome measures include depressive symptoms, anxiety symptoms, and sleep quality measured by self-reported questionnaires. We will investigate the studies for additional outcomes addressing adverse effects and resting-state electroencephalography analysis. Additionally, all types of nonrandomized studies will be included for a narrative synthesis. The intended and unintended effects of nonrandomized studies will be extracted and summarized in a descriptive table. thics approval is not required for a systematic review, as there will be no patient involvement. The search for this systematic review commenced in July 2020, and we expect to publish the findings in early 2021. his systematic review will provide recommendations for researchers and health professionals, as well as people with chronic pain, about the evidence for the analgesic effect of electroencephalographic neurofeedback. nternational Prospective Register of Systematic Reviews (PROSPERO) CRD42020177608 www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=177608 RR1-10.2196/22821
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2022
Publisher: IEEE
Date: 08-2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2010
Publisher: Hogrefe Publishing Group
Date: 06-2010
DOI: 10.1024/1662-9647/A000014
Abstract: Electroencephalography (EEG) has been widely adopted to monitor changes in cognitive states, particularly stages of sleep, as EEG recordings contain a wealth of information reflecting changes in alertness and sleepiness. In this study, silicon dry electrodes based on Micro-Electro-Mechanical Systems (MEMS) were developed to bring high-quality EEG acquisition to operational workplaces. They have superior conductivity performance, large signal intensity, and are smaller in size than conventional (wet) electrodes. An EEG-based drowsiness estimation system consisting of a dry-electrode array, power spectrum estimation, principal component analysis (PCA)-based EEG signal analysis, and multivariate linear regression was developed to estimate drivers’ drowsiness levels in a virtual-reality-based dynamic driving simulator. The proposed system can help elders who are often affected by periods of tiredness and fatigue.
Publisher: IEEE
Date: 2006
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Springer Science and Business Media LLC
Date: 12-2009
DOI: 10.1155/2009/854806
Publisher: IEEE
Date: 12-2019
Publisher: IEEE
Date: 10-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2016
Publisher: Elsevier BV
Date: 2017
Publisher: Springer Science and Business Media LLC
Date: 17-11-2005
Publisher: IEEE
Date: 2003
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 1999
DOI: 10.1109/3477.809028
Abstract: This paper proposes a neuro-fuzzy combiner (NFC) with reinforcement learning capability for solving multiobjective control problems. The proposed NFC can combine n existing low-level controllers in a hierarchical way to form a multiobjective fuzzy controller. It is assumed that each low-level (fuzzy or nonfuzzy) controller has been well designed to serve a particular objective. The role of the NFC is to fuse the n actions decided by the n low-level controllers and determine a proper action acting on the environment (plant) at each time step. Hence, the NFC can combine low-level controllers and achieve multiple objectives (goals) at once. The NFC acts like a switch that chooses a proper action from the actions of low-level controllers according to the feedback information from the environment. In fact, the NFC is a soft switch it allows more than one low-level actions to be active with different degrees through fuzzy combination at each time step. An NFC can be designed by the trial-and-error approach if enough a priori knowledge is available, or it can be obtained by supervised learning if precise input/output training data are available. In the more practical cases when there is no instructive teaching information available, the NFC can learn by itself using the proposed reinforcement learning scheme. Adopted with reinforcement learning capability, the NFC can learn to achieve desired multiobjectives simultaneously through the rough reinforcement feedback from the environment, which contains only critic information such as "success (good)" or "failure (bad)" for each desired objective. Computer simulations have been conducted to illustrate the performance and applicability of the proposed architecture and learning scheme.
Publisher: Springer Science and Business Media LLC
Date: 21-01-2015
Publisher: Springer International Publishing
Date: 2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2008
Publisher: IEEE
Date: 08-2007
Publisher: IEEE
Date: 07-2015
Publisher: IEEE
Date: 07-2020
Publisher: American Scientific Publishers
Date: 06-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2023
Publisher: IEEE
Date: 06-2008
Publisher: Elsevier BV
Date: 02-2016
Publisher: American Scientific Publishers
Date: 12-2012
Publisher: MDPI AG
Date: 19-08-2022
DOI: 10.3390/S22166230
Abstract: This paper discusses a novel approach to an EEG (electroencephalogram)-based driver distraction classification by using brain connectivity estimators as features. Ten healthy volunteers with more than one year of driving experience and an average age of 24.3 participated in a virtual reality environment with two conditions, a simple math problem-solving task and a lane-keeping task to mimic the distracted driving task and a non-distracted driving task, respectively. Independent component analysis (ICA) was conducted on the selected epochs of six selected components relevant to the frontal, central, parietal, occipital, left motor, and right motor areas. Granger–Geweke causality (GGC), directed transfer function (DTF), partial directed coherence (PDC), and generalized partial directed coherence (GPDC) brain connectivity estimators were used to calculate the connectivity matrixes. These connectivity matrixes were used as features to train the support vector machine (SVM) with the radial basis function (RBF) and classify the distracted and non-distracted driving tasks. GGC, DTF, PDC, and GPDC connectivity estimators yielded the classification accuracies of 82.27%, 70.02%, 86.19%, and 80.95%, respectively. Further analysis of the PDC connectivity estimator was conducted to determine the best window to differentiate between the distracted and non-distracted driving tasks. This study suggests that the PDC connectivity estimator can yield better classification accuracy for driver distractions.
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Springer Science and Business Media LLC
Date: 31-03-2008
DOI: 10.1155/2008/849040
Publisher: IEEE
Date: 05-2008
Publisher: MDPI AG
Date: 09-05-2022
Abstract: Introduction: The autonomic nervous system plays a vital role in the modulation of many vital bodily functions, one of which is sleep and wakefulness. Many studies have investigated the link between autonomic dysfunction and sleep cycles however, few studies have investigated the links between short-term sleep health, as determined by the Pittsburgh Quality of Sleep Index (PSQI), such as subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction, and autonomic functioning in healthy in iduals. Aim: In this cross-sectional study, the aim was to investigate the links between short-term sleep quality and duration, and heart rate variability in 60 healthy in iduals, in order to provide useful information about the effects of stress and sleep on heart rate variability (HRV) indices, which in turn could be integrated into biological models for wearable devices. Methods: Sleep parameters were collected from participants on commencement of the study, and HRV was derived using an electrocardiogram (ECG) during a resting and stress task (Trier Stress Test). Result: Low-frequency to high-frequency (LF:HF) ratio was significantly higher during the stress task than during the baseline resting phase, and very-low-frequency and high-frequency HRV were inversely related to impaired sleep during stress tasks. Conclusion: Given the ubiquitous nature of wearable technologies for monitoring health states, in particular HRV, it is important to consider the impacts of sleep states when using these technologies to interpret data. Very-low-frequency HRV during the stress task was found to be inversely related to three negative sleep indices: sleep quality, daytime dysfunction, and global sleep score.
Publisher: IEEE
Date: 10-2010
Publisher: Institution of Engineering and Technology
Date: 08-06-2022
DOI: 10.1049/PBTE104E_CH4
Publisher: IEEE
Date: 04-2007
Publisher: IEEE
Date: 08-2011
Publisher: Elsevier BV
Date: 05-2012
DOI: 10.1016/J.NEUROIMAGE.2012.02.008
Abstract: This study investigates the temporal brain dynamics associated with haptic feedback in a visuomotor tracking task. Haptic feedback with deviation-related forces was used throughout tracking experiments in which subjects' behavioral responses and electroencephalogram (EEG) data were simultaneously measured. Independent component analysis was employed to decompose the acquired EEG signals into temporally independent time courses arising from distinct brain sources. Clustering analysis was used to extract independent components that were comparable across participants. The resultant independent brain processes were further analyzed via time-frequency analysis (event-related spectral perturbation) and event-related coherence (ERCOH) to contrast brain activity during tracking experiments with or without haptic feedback. Across subjects, in epochs with haptic feedback, components with equivalent dipoles in or near the right motor region exhibited greater alpha band power suppression. Components with equivalent dipoles in or near the left frontal, central, left motor, right motor, and parietal regions exhibited greater beta-band power suppression, while components with equivalent dipoles in or near the left frontal, left motor, and right motor regions showed greater gamma-band power suppression relative to non-haptic conditions. In contrast, the right occipital component cluster exhibited less beta-band power suppression in epochs with haptic feedback compared to non-haptic conditions. The results of ERCOH analysis of the six component clusters showed that there were significant increases in coherence between different brain networks in response to haptic feedback relative to the coherence observed when haptic feedback was not present. The results of this study provide novel insight into the effects of haptic feedback on the brain and may aid the development of new tools to facilitate the learning of motor skills.
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Wiley
Date: 10-12-2022
DOI: 10.1111/ENE.15189
Abstract: Electroencephalographic (EEG) neurofeedback has been utilized to regulate abnormal brain activity associated with chronic pain. In this systematic review, we synthesized the evidence from randomized controlled trials (RCTs) to evaluate the effect of EEG neurofeedback on chronic pain using random effects meta‐analyses. Additionally, we performed a narrative review to explore the results of non‐randomized studies. The quality of included studies was assessed using Cochrane risk of bias tools, and the GRADE system was used to rate the certainty of evidence. Ten RCTs and 13 non‐randomized studies were included. The primary meta‐analysis on nine eligible RCTs indicated that although there is low confidence, EEG neurofeedback may have a clinically meaningful effect on pain intensity in short‐term. Removing the studies with high risk of bias from the primary meta‐analysis resulted in moderate confidence that there remained a clinically meaningful effect on pain intensity. We could not draw any conclusion from the findings of non‐randomized studies, as they were mostly non‐comparative trials or explorative case series. However, the extracted data indicated that the neurofeedback protocols in both RCTs and non‐randomized studies mainly involved the conventional EEG neurofeedback approach, which targeted reinforcing either alpha or sensorimotor rhythms and suppressing theta and/or beta bands on one brain region at a time. A posthoc analysis of RCTs utilizing the conventional approach resulted in a clinically meaningful effect estimate for pain intensity. Although there is promising evidence on the analgesic effect of EEG neurofeedback, further studies with larger s le sizes and higher quality of evidence are required.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2009
Publisher: IEEE
Date: 2004
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 1999
DOI: 10.1109/3477.809037
Abstract: In this paper, new techniques for deformed image motion estimation and compensation using variable-size block-matching are proposed, which can be applied to an image sequence compression system or a moving object recognition system. The motion estimation and compensation techniques have been successfully applied in the area of image sequence coding. Many research papers on improving the performance of these techniques have been published many directions are proposed, which can all lead to better performance than the conventional techniques. Among them, both generalized block-matching and variable-size block-matching are successfully applied in reducing the data rate of compensation error and motion information, respectively. These two algorithms have their merits, but suffer from their drawbacks. Moreover, reducing the data rate in compensation error is sometimes increasing the data rate in motion information, or vice versa. Based on these two algorithms, we propose and examine several algorithms which are effective in reducing the data rate. We then incorporate these algorithms into a system, in which they work together to overcome the disadvantages to in idual and keep their merits at the same time. The proposed system can optimally balance the amount of data rate in two aspects (i.e., compensation error and motion information). Experimental results show that the proposed system outweighs the conventional techniques. Since we propose a recovery operation which tries to recover the incorrect motion vectors from the global motion, this proposed system can also be applied to the moving object recognition in image sequences.
Publisher: IEEE
Date: 07-2010
Publisher: Frontiers Media SA
Date: 12-11-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-1996
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2019
Publisher: IEEE
Date: 06-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2002
DOI: 10.1109/91.983276
Publisher: IEEE
Date: 2001
Publisher: SAGE Publications
Date: 03-2005
Abstract: The objective of this paper is to develop a multipurpose virtual-reality (VR) dynamic simulation system to meet the requirements of public security in the training of human operators. In this way, the operator can feel that he or she is controlling a real machine or vehicle to achieve the objective of real training. The developed VR dynamical simulation system in this paper mainly consists of three elements: a six-degree-of-freedom motion platform (Stewart platform), a force-reflection joystick, and an interactive VR scene. In the developed VR dynamic simulation system, the operator could sit on a Stewart platform to feel the velocity and orientation of motion, and could handle a force-reflection joystick to transfer the commands to the VR scene. Then, the operator will receive the force feedback from the Stewart platform and the joystick. Finally, a flight simulation scene is applied to illustrate the effectiveness of the developed VR dynamical simulation system. Experimental results demonstrate that the evaluation of the VR dynamical simulation system is comparatively good.
Publisher: IEEE
Date: 06-2011
Publisher: arXiv
Date: 2019
Publisher: IEEE
Date: 09-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2013
Publisher: Elsevier BV
Date: 04-1999
Publisher: IEEE
Date: 08-2012
Publisher: IEEE
Date: 02-07-2023
Publisher: IEEE
Date: 05-2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 1994
DOI: 10.1109/91.273126
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2016
Publisher: Springer Science and Business Media LLC
Date: 19-03-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2020
Publisher: IEEE
Date: 2005
Publisher: Institution of Engineering and Technology (IET)
Date: 2006
Publisher: Wiley
Date: 2008
DOI: 10.1002/JEMT.20555
Abstract: Systemic analysis of subcellular protein localization (location proteomics) provides clues for understanding gene functions and physiological condition of the cells. However, recognition of cell images of subcellular structures highly depends on experience and becomes the rate-limiting step when classifying subcellular protein localization. Several research groups have extracted specific numerical features for the recognition of subcellular protein localization, but these recognition systems are restricted to images of single particular cell line acquired by one specific imaging system and not applied to recognize a range of cell image sources. In this study, we establish a single system for automated subcellular structure recognition to identify cell images from various sources. Two different sources of cell images, 317 Vero (gfp-cdna.embl.de) and 875 CHO cell images of subcellular structures, were used to train and test the system. When the system was trained by a single source of images, the recognition rate is high and specific to the trained source. The system trained by the CHO cell images gave high average recognition accuracy for CHO cells of 96%, but this was reduced to 46% with Vero images. When we trained the system using a mixture of CHO and Vero cell images, an average accuracy of recognition reached 86.6% for both CHO and Vero cell images. The system can reject images with low confidence and identify the cell images correctly recognized to avoid manual reconfirmation. In summary, we have established a single system that can recognize subcellular protein localizations from two different sources for location-proteomic studies. studies.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: IEEE
Date: 07-2010
Publisher: Adjacent Digital Politics Ltd
Date: 13-04-2023
Abstract: Chin-Teng Lin, Distinguished Professor from the University of Technology Sydney, Human-centric AI Centre, AAII, explores human-centric AI, focusing on frontier research & building industry capability. Distinguished Professor Chin-Teng Lin is a pioneer, inventor and world leader in computational intelligence and co-founder of the GrapheneX-UTS Human-centric AI Centre (HAI) at the University of Technology Sydney, Australia. For the past three decades, he has significantly advanced artificial intelligence (AI), brain-computer interfaces and human-AI teaming across theory, methodology and applications. Here, he provides an overview of his frontier research in combining human intelligence with AI to enable humans to make better decisions in complex, stressful situations and what it takes to translate research into tangible products and services. He also highlights the importance of ethical considerations when teaming humans with robots.
Publisher: IEEE
Date: 2006
Publisher: Walter de Gruyter GmbH
Date: 2011
DOI: 10.1515/RNS.2011.047
Publisher: International Journal of Automation and Smart Technology
Date: 09-2012
Publisher: IEEE
Date: 05-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2002
DOI: 10.1109/91.995116
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2010
Publisher: arXiv
Date: 2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2004
Publisher: Informa Healthcare
Date: 11-2010
DOI: 10.1517/17530059.2010.512363
Abstract: Cancer resistance to conventional therapies has been attributed to cancer stem cells (CSCs). Although a variety of markers have been reported, a universal marker has not yet been found to identify CSCs. Better identification of these CSCs may lead to new therapies that selectively target these cells and thereby result in more effective treatment. This article categorizes the types of marker that have been identified and explores their potential diagnostic and therapeutic value. A focused literature review of studies relating to CSCs and their identification was conducted. Databases evaluated include MEDLINE and Web of Science through 2009. The ideal identification method needs to be effective and practical in terms of application. The measurement of aldehyde dehydrogenase activity is simple to accomplish compared with other reported identification methods however, cell surface antigens have been studied most frequently in the therapeutic targeting of CSCs. Although specific targeting methods have been reported for various cancers, there does not appear to be a proven universal marker for CSCs that would apply to all cancers. Each particular identification method appears to have advantages and disadvantages. From a therapeutic standpoint, targeting of these CSCs should improve prognosis.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2001
DOI: 10.1109/3477.931548
Abstract: The stability analysis of the learning rate for a two-layer neural network (NN) is discussed first by minimizing the total squared error between the actual and desired outputs for a set of training vectors. The stable and optimal learning rate, in the sense of maximum error reduction, for each iteration in the training (back propagation) process can therefore be found for this two-layer NN. It has also been proven in this paper that the dynamic stable learning rate for this two-layer NN must be greater than zero. Thus it Is guaranteed that the maximum error reduction can be achieved by choosing the optimal learning rate for the next training iteration. A dynamic fuzzy neural network (FNN) that consists of the fuzzy linguistic process as the premise part and the two-layer NN as the consequence part is then illustrated as an immediate application of our approach. Each part of this dynamic FNN has its own learning rate for training purpose. A genetic algorithm is designed to allow a more efficient tuning process of the two learning rates of the FNN. The objective of the genetic algorithm is to reduce the searching time by searching for only one learning rate, which is the learning rate of the premise part, in the FNN. The dynamic optimal learning rates of the two-layer NN can be found directly using our innovative approach. Several ex les are fully illustrated and excellent results are obtained for the model car backing up problem and the identification of nonlinear first order and second order systems.
Publisher: Springer Science and Business Media LLC
Date: 25-01-2020
Publisher: IEEE
Date: 11-2012
Publisher: IEEE
Date: 05-2011
Publisher: IEEE
Date: 06-2011
Publisher: IEEE
Date: 1992
Publisher: Springer Science and Business Media LLC
Date: 09-10-2008
Abstract: The Signal-to-Noise-Ratio (SNR) is often used for identification of biomarkers for two-class problems and no formal and useful generalization of SNR is available for multiclass problems. We propose innovative generalizations of SNR for multiclass cancer discrimination through introduction of two indices, Gene Dominant Index and Gene Dormant Index (GDIs). These two indices lead to the concepts of dominant and dormant genes with biological significance. We use these indices to develop methodologies for discovery of dominant and dormant biomarkers with interesting biological significance. The dominancy and dormancy of the identified biomarkers and their excellent discriminating power are also demonstrated pictorially using the scatterplot of in idual gene and 2-D Sammon's projection of the selected set of genes. Using information from the literature we have shown that the GDI based method can identify dominant and dormant genes that play significant roles in cancer biology. These biomarkers are also used to design diagnostic prediction systems. To evaluate the effectiveness of the GDIs, we have used four multiclass cancer data sets (Small Round Blue Cell Tumors, Leukemia, Central Nervous System Tumors, and Lung Cancer). For each data set we demonstrate that the new indices can find biologically meaningful genes that can act as biomarkers. We then use six machine learning tools, Nearest Neighbor Classifier (NNC), Nearest Mean Classifier (NMC), Support Vector Machine (SVM) classifier with linear kernel, and SVM classifier with Gaussian kernel, where both SVMs are used in conjunction with one-vs-all (OVA) and one-vs-one (OVO) strategies. We found GDIs to be very effective in identifying biomarkers with strong class specific signatures. With all six tools and for all data sets we could achieve better or comparable prediction accuracies usually with fewer marker genes than results reported in the literature using the same computational protocols. The dominant genes are usually easy to find while good dormant genes may not always be available as dormant genes require stronger constraints to be satisfied but when they are available, they can be used for authentication of diagnosis. Since GDI based schemes can find a small set of dominant/dormant biomarkers that is adequate to design diagnostic prediction systems, it opens up the possibility of using real-time qPCR assays or antibody based methods such as ELISA for an easy and low cost diagnosis of diseases. The dominant and dormant genes found by GDIs can be used in different ways to design more reliable diagnostic prediction systems.
Publisher: IEEE
Date: 10-2009
Publisher: IEEE
Date: 2004
Publisher: IEEE
Date: 1996
Publisher: IEEE
Date: 07-2010
Publisher: IEEE
Date: 11-2017
Publisher: Springer Singapore
Date: 2015
Publisher: Elsevier BV
Date: 09-2023
Publisher: IEEE
Date: 11-2017
Publisher: Elsevier BV
Date: 02-2020
Publisher: IEEE
Date: 2004
Publisher: SPIE-Intl Soc Optical Eng
Date: 07-05-2012
Publisher: arXiv
Date: 2018
Publisher: arXiv
Date: 2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2018
Publisher: Walter de Gruyter GmbH
Date: 03-05-2017
Abstract: Electroencephalograph (EEG) data provide insight into the interconnections and relationships between various cognitive states and their corresponding brain dynamics, by demonstrating dynamic connections between brain regions at different frequency bands. While sensory input tends to stimulate neural activity in different frequency bands, peaceful states of being and self-induced meditation tend to produce activity in the mid-range (Alpha). These studies were conducted with the aim of: (a) testing different equipment in order to assess two (2) different EEG technologies together with their benefits and limitations and (b) having an initial impression of different brain states associated with different experimental modalities and tasks, by analyzing the spatial and temporal power spectrum and applying our movie making methodology to engage in qualitative exploration via the art of encephalography. This study complements our previous study of measuring multichannel EEG brain dynamics using MINDO48 equipment associated with three experimental modalities measured both in the laboratory and the natural environment. Together with Hilbert analysis, we conjecture, the results will provide us with the tools to engage in more complex brain dynamics and mental states, such as Meditation, Mathematical Audio Lectures, Music Induced Meditation, and Mental Arithmetic Exercises. This paper focuses on open eye and closed eye conditions, as well as meditation states in laboratory conditions. We assess similarities and differences between experimental modalities and their associated brain states as well as differences between the different tools for analysis and equipment.
Publisher: Elsevier BV
Date: 07-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2017
Publisher: Elsevier BV
Date: 02-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-1995
DOI: 10.1109/21.370198
Publisher: IEEE
Date: 05-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Springer Science and Business Media LLC
Date: 22-03-2017
Publisher: IEEE
Date: 2004
Publisher: Springer Science and Business Media LLC
Date: 05-04-2019
DOI: 10.1038/S41597-019-0027-4
Abstract: We describe driver behaviour and brain dynamics acquired from a 90-minute sustained-attention task in an immersive driving simulator. The data included 62 sessions of 32-channel electroencephalography (EEG) data for 27 subjects driving on a four-lane highway who were instructed to keep the car cruising in the centre of the lane. Lane-departure events were randomly induced to cause the car to drift from the original cruising lane towards the left or right lane. A complete trial included events with deviation onset, response onset, and response offset. The next trial, in which the subject was instructed to drive back to the original cruising lane, began 5–10 seconds after finishing the previous trial. We believe that this dataset will lead to the development of novel neural processing methodology that can be used to index brain cortical dynamics and detect driving fatigue and drowsiness. This publicly available dataset will be beneficial to the neuroscience and brain-computer interface communities.
Publisher: Springer Singapore
Date: 10-08-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-05-2022
DOI: 10.36227/TECHRXIV.19691914
Abstract: Clustering is a fundamental tool of scientific analysis, ubiquitous in disciplines from biology and chemistry to astronomy and pattern recognition. We propose a novel clustering algorithm based on the natural idea that a cluster and its nearest neighbor with higher mass should be merged into one cluster, unless they both have relatively large masses and the distance between them is also relatively large. The find of mass and distance peaks reveals the mergers that don’t conform to the rule and should be removed. The algorithm is parameter-free and harnesses this idea to recognize any cluster and find the proper number of clusters and noise autonomously. Experiments on numerous synthetic and real-world data sets show the enormous versatility of the proposed algorithm that remarkably outperforms the best compared algorithm. Additionally, we also compare it with latest state-of-the-art deep clustering algorithms on several challenging image data sets. The proposed algorithm without any deep representation achieves better or close performance than deep clustering algorithms on image clustering.
Publisher: IEEE
Date: 11-2013
Publisher: IEEE
Date: 06-2009
DOI: 10.1109/BIBE.2009.65
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2021
Publisher: MDPI AG
Date: 08-12-2021
DOI: 10.20944/PREPRINTS202112.0140.V1
Abstract: Accuracy and computational cost are the main challenges of deep neural networks in image recognition. This paper proposes an efficient ranking reduction to binary classification approach using a new feed-forward network and feature selection based on ranking the image pixels. Preference net (PN) is a novel deep ranking learning approach based on Preference Neural Network (PNN), which uses new ranking objective function and positive smooth staircase (PSS) activation function to accelerate the image pixels& rsquo ranking. PN has a new type of weighted kernel based on spearman ranking correlation instead of convolution to build the features matrix. The PN employs multiple kernels that have different sizes to partial rank image pixels& rsquo in order to find the best features sequence. PN consists of multiple PNNs& rsquo have shared output layer. Each ranker kernel has a separate PNN. The output results are converted to classification accuracy using the score function. PN has promising results comparing to the latest deep learning (DL) networks using the weighted average ensemble of each PN models for each kernel on CFAR-10 and Mnist-Fashion datasets in terms of accuracy and less computational cost.
Publisher: IEEE
Date: 05-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2007
Abstract: The classification of protein structures is essential for their function determination in bioinformatics. At present, a reasonably high rate of prediction accuracy has been achieved in classifying proteins into four classes in the SCOP database according to their primary amino acid sequences. However, for further classification into fine-grained folding categories, especially when the number of possible folding patterns as those defined in the SCOP database is large, it is still quite a challenge. In our previous work, we have proposed a two-level classification strategy called hierarchical learning architecture (HLA) using neural networks and two indirect coding features to differentiate proteins according to their classes and folding patterns, which achieved an accuracy rate of 65.5%. In this paper, we use a combinatorial fusion technique to facilitate feature selection and combination for improving predictive accuracy in protein structure classification. When applying various criteria in combinatorial fusion to the protein fold prediction approach using neural networks with HLA and the radial basis function network (RBFN), the resulting classification has an overall prediction accuracy rate of 87% for four classes and 69.6% for 27 folding categories. These rates are significantly higher than the accuracy rate of 56.5% previously obtained by Ding and Dubchak. Our results demonstrate that data fusion is a viable method for feature selection and combination in the prediction and classification of protein structure.
Publisher: IEEE
Date: 2005
Publisher: IEEE
Date: 06-2016
Publisher: IEEE
Date: 1997
Publisher: IEEE
Date: 05-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Cold Spring Harbor Laboratory
Date: 03-02-2021
DOI: 10.1101/2021.02.02.428702
Abstract: We have designed tracking and collision prediction tasks to elucidate the differences in the physiological response to the workload variations in basic ATC tasks to untangle the impact of workload variations experienced by operators working in a complex ATC environment. Even though several factors influence the complexity of ATC tasks, keeping track of the aircraft and preventing collision are the most crucial. Physiological measures, such as electroencephalogram (EEG), eye activity, and heart rate variability (HRV) data, were recorded from 24 participants performing tracking and collision prediction tasks with three levels of difficulty. The neurometrics of workload variations in the tracking and collision prediction tasks were markedly distinct, indicating that neurometrics can provide insights on the type of mental workload. The pupil size, number of blinks and HRV metric, root mean square of successive difference (RMSSD), varied significantly with the mental workload in both these tasks in a similar manner. Our findings indicate that variations in task load are sensitively reflected in physiological signals, such as EEG, eye activity and HRV, in these basic ATC-related tasks. These findings have applicability to the design of future mental workload adaptive systems that integrate neurometrics in deciding not just ‘when’ but also ‘what’ to adapt. Our study provides compelling evidence in the viability of developing intelligent closed-loop mental workload adaptive systems that ensure efficiency and safety in ATC and beyond. This article identifies the physiological correlates of mental workload variation in basic ATC tasks. The findings assert that neurometrics can provide more information on the task that contributes to the workload, which can aid in the design of intelligent mental workload adaptive system.
Publisher: IEEE
Date: 06-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2001
DOI: 10.1109/3468.983423
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Frontiers Media SA
Date: 31-07-2017
Publisher: IEEE
Date: 05-2012
Publisher: IEEE
Date: 2002
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2009
Publisher: Springer Berlin Heidelberg
Date: 2002
Publisher: Frontiers Media SA
Date: 04-2021
DOI: 10.3389/FNINS.2021.651762
Abstract: With the advent of advanced machine learning methods, the performance of brain–computer interfaces (BCIs) has improved unprecedentedly. However, electroencephalography (EEG), a commonly used brain imaging method for BCI, is characterized by a tedious experimental setup, frequent data loss due to artifacts, and is time consuming for bulk trial recordings to take advantage of the capabilities of deep learning classifiers. Some studies have tried to address this issue by generating artificial EEG signals. However, a few of these methods are limited in retaining the prominent features or biomarker of the signal. And, other deep learning-based generative methods require a huge number of s les for training, and a majority of these models can handle data augmentation of one category or class of data at any training session. Therefore, there exists a necessity for a generative model that can generate synthetic EEG s les with as few available trials as possible and generate multi-class while retaining the biomarker of the signal. Since EEG signal represents an accumulation of action potentials from neuronal populations beneath the scalp surface and as spiking neural network (SNN), a biologically closer artificial neural network, communicates via spiking behavior, we propose an SNN-based approach using surrogate-gradient descent learning to reconstruct and generate multi-class artificial EEG signals from just a few original s les. The network was employed for augmenting motor imagery (MI) and steady-state visually evoked potential (SSVEP) data. These artificial data are further validated through classification and correlation metrics to assess its resemblance with original data and in-turn enhanced the MI classification performance.
Publisher: Research Square Platform LLC
Date: 26-01-2021
DOI: 10.21203/RS.3.RS-108085/V1
Abstract: Research and development of electroencephalogram (EEG) based brain-computer interfaces (BCIs) have advanced rapidly, partly due to the wide adoption of sophisticated machine learning approaches for decoding the EEG signals. However, recent studies have shown that machine learning algorithms are vulnerable to adversarial attacks, e.g., the attacker can add tiny adversarial perturbations to a test s le to fool the model, or poison the training data to insert a secret backdoor. Previous research has shown that adversarial attacks are also possible for EEG-based BCIs. However, only adversarial perturbations have been considered, and the approaches are theoretically sound but very difficult to implement in practice. This article proposes to use narrow period pulse for poisoning attack of EEG-based BCIs, which is more feasible in practice and has never been considered before. One can create dangerous backdoors in the machine learning model by injecting poisoning s les into the training set. Test s les with the backdoor key will then be classified into the target class specified by the attacker. What most distinguishes our approach from previous ones is that the backdoor key does not need to be synchronized with the EEG trials, making it very easy to implement. The effectiveness and robustness of the backdoor attack approach is demonstrated, highlighting a critical security concern for EEG-based BCIs.
Publisher: IEEE
Date: 2009
Publisher: IEEE
Date: 2010
Publisher: ACM
Date: 14-11-2019
Publisher: IEEE
Date: 2004
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: IEEE
Date: 12-2013
Publisher: IEEE
Date: 06-2009
Publisher: Elsevier BV
Date: 05-2015
Publisher: IEEE
Date: 07-2020
Publisher: IEEE
Date: 2005
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2012
Publisher: Elsevier BV
Date: 09-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: IEEE
Date: 05-2008
Publisher: IMR Press
Date: 2020
Publisher: IEEE
Date: 08-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2018
Publisher: arXiv
Date: 2019
Publisher: IEEE
Date: 2004
Publisher: IEEE
Date: 07-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2013
Publisher: World Scientific Pub Co Pte Lt
Date: 02-1999
DOI: 10.1142/S0218488599000027
Abstract: In this paper, we propose a speech recognition algorithm which utilizes hidden Markov models (HMM) and Viterbi algorithm for segmenting the input speech sequence, such that the variable-dimensional speech signal is converted into a fixed-dimensional speech signal, called TN vector. We then use the fuzzy perceptron to generate hyperplanes which separate patterns of each class from the others. The proposed speech recognition algorithm is easy for speaker adaptation when the idea of "supporting pattern" is used. The supporting patterns are those patterns closest to the hyperplane. When a recognition error occurs, we include all the TN vectors of the input speech sequence with respect to the segmentations of all HMM models as the supporting patterns. The supporting patterns are then used by the fuzzy perceptron to tune the hyperplane that can cause correct recognition, and also tune the hyperplane that resulted in wrong recognition. Since only two hyperplane need to be tuned for a recognition error, the proposed adaptation scheme is time-economic and suitable for on-line adaptation. Although the adaptation scheme cannot ensure to correct the wrong recognition right after adaptation, the hyperplanes are tuned in the direction for correct recognition iteratively and the speed of adaptation can be adjusted by a "belief" parameter set by the user. Several ex les are used to show the performance of the proposed speech recognition algorithm and the speaker adaptation scheme.
Publisher: Springer Science and Business Media LLC
Date: 21-03-2014
Publisher: IEEE
Date: 2005
Publisher: Frontiers Media SA
Date: 14-07-2020
Publisher: JMIR Publications Inc.
Date: 03-06-2020
Abstract: europathic pain is a debilitating secondary condition for many in iduals with spinal cord injury. Spinal cord injury neuropathic pain often is poorly responsive to existing pharmacological and nonpharmacological treatments. A growing body of evidence supports the potential for brain-computer interface systems to reduce spinal cord injury neuropathic pain via electroencephalographic neurofeedback. However, further studies are needed to provide more definitive evidence regarding the effectiveness of this intervention. he primary objective of this study is to evaluate the effectiveness of a multiday course of a brain-computer interface neuromodulative intervention in a gaming environment to provide pain relief for in iduals with neuropathic pain following spinal cord injury. e have developed a novel brain-computer interface-based neuromodulative intervention for spinal cord injury neuropathic pain. Our brain-computer interface neuromodulative treatment includes an interactive gaming interface, and a neuromodulation protocol targeted to suppress theta (4-8 Hz) and high beta (20-30 Hz) frequency powers, and enhance alpha (9-12 Hz) power. We will use a single-case experimental design with multiple baselines to examine the effectiveness of our self-developed brain-computer interface neuromodulative intervention for the treatment of spinal cord injury neuropathic pain. We will recruit 3 participants with spinal cord injury neuropathic pain. Each participant will be randomly allocated to a different baseline phase (ie, 7, 10, or 14 days), which will then be followed by 20 sessions of a 30-minute brain-computer interface neuromodulative intervention over a 4-week period. The visual analog scale assessing average pain intensity will serve as the primary outcome measure. We will also assess pain interference as a secondary outcome domain. Generalization measures will assess quality of life, sleep quality, and anxiety and depressive symptoms, as well as resting-state electroencephalography and thalamic γ-aminobutyric acid concentration. his study was approved by the Human Research Committees of the University of New South Wales in July 2019 and the University of Technology Sydney in January 2020. We plan to begin the trial in October 2020 and expect to publish the results by the end of 2021. his clinical trial using single-case experimental design methodology has been designed to evaluate the effectiveness of a novel brain-computer interface neuromodulative treatment for people with neuropathic pain after spinal cord injury. Single-case experimental designs are considered a viable alternative approach to randomized clinical trials to identify evidence-based practices in the field of technology-based health interventions when recruitment of large s les is not feasible. ustralian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12620000556943 bit.ly/2RY1jRx RR1-10.2196/20979
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2014
Publisher: Springer Science and Business Media LLC
Date: 29-09-2022
DOI: 10.1007/S42979-022-01407-3
Abstract: In recent years, instance segmentation has become a key research area in computer vision. This technology has been applied in varied applications such as robotics, healthcare and intelligent driving. Instance segmentation technology not only detects the location of the object but also marks edges for each single instance, which can solve both object detection and semantic segmentation concurrently. Our survey will give a detail introduction to the instance segmentation technology based on deep learning, reinforcement learning and transformers. Further, we will discuss about its development in this field along with the most common datasets used. We will also focus on different challenges and future development scope for instance segmentation. This technology will provide a strong reference for future researchers in our survey paper.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2016
Publisher: Springer International Publishing
Date: 2017
Publisher: Springer Science and Business Media LLC
Date: 19-07-2022
DOI: 10.1038/S41539-022-00132-Z
Abstract: Although beacon- and map-based spatial strategies are the default strategies for navigation activities, today’s navigational aids mostly follow a beacon-based design where one is provided with turn-by-turn instructions. Recent research, however, shows that our reliance on these navigational aids is causing a decline in our spatial skills. We are processing less of our surrounding environment and relying too heavily on the instructions given. To reverse this decline, we need to engage more in map-based learning, which encourages the user to process and integrate spatial knowledge into a cognitive map built to benefit flexible and independent spatial navigation behaviour. In an attempt to curb our loss of skills, we proposed a navigation assistant to support map-based learning during active navigation. Called the virtual global landmark (VGL) system, this augmented reality (AR) system is based on the kinds of techniques used in traditional orienteering. Specifically, a notable landmark is always present in the user’s sight, allowing the user to continuously compute where they are in relation to that specific location. The efficacy of the unit as a navigational aid was tested in an experiment with 27 students from the University of Technology Sydney via a comparison of brain dynamics and behaviour. From an analysis of behaviour and event-related spectral perturbation, we found that participants were encouraged to process more spatial information with a map-based strategy where a silhouette of the compass-like landmark was perpetually in view. As a result of this technique, they consistently navigated with greater efficiency and better accuracy.
Publisher: IEEE
Date: 04-2013
Publisher: Informa UK Limited
Date: 10-1996
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2018
Publisher: IEEE
Date: 10-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2001
DOI: 10.1109/3477.907566
Abstract: This paper discusses the problem of automatic word boundary detection in the presence of variable-level background noise. Commonly used robust word boundary detection algorithms always assume that the background noise level is fixed. In fact, the background noise level may vary during the procedure of recording. This is the major reason that most robust word boundary detection algorithms cannot work well in the condition of variable background noise level. In order to solve this problem, we first propose a refined time-frequency (RTF) parameter for extracting both the time and frequency features of noisy speech signals. The RTF parameter extends the (time-frequency) TF parameter proposed by Junqua et al. from single band to multiband spectrum analysis, where the frequency bands help to make the distinction between speech signal and noise clear. The RTF parameter can extract useful frequency information. Based on this RTF parameter, we further propose a new word boundary detection algorithm by using a recurrent self-organizing neural fuzzy inference network (RSONFIN). Since RSONPIN can process the temporal relations, the proposed RTF-based RSONFIN algorithm can find the variation of the background noise level and detect correct word boundaries in the condition of variable background noise level. As compared to normal neural networks, the RSONFIN can always find itself an economic network size with high-learning speed. Due to the self-learning ability of RSONFIN, this RTF-based RSONFIN algorithm avoids the need for empirically determining ambiguous decision rules in normal word boundary detection algorithms. Experimental results show that this new algorithm achieves higher recognition rate than the TF-based algorithm which has been shown to outperform several commonly used word boundary detection algorithms by about 12% in variable background noise level condition, It also reduces the recognition error rate due to endpoint detection to about 23%, compared to an average of 47% obtained by the TF-based algorithm in the same condition.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2012
Publisher: IEEE
Date: 10-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2023
Publisher: IEEE
Date: 07-2013
Publisher: IEEE
Date: 10-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2021
Publisher: Elsevier BV
Date: 11-1998
Publisher: IEEE
Date: 10-2016
DOI: 10.1109/SMC.2015.560
Publisher: IEEE
Date: 04-2013
Publisher: Elsevier BV
Date: 09-2012
DOI: 10.1016/J.NEUROIMAGE.2012.05.035
Abstract: This study investigates the independent modulators that mediate the power spectra of electrophysiological processes, measured by electroencephalogram (EEG), in a sustained-attention experiment. EEG and behavioral data were collected during 1-2 hour virtual-reality based driving experiments in which subjects were instructed to maintain their cruising position and compensate for randomly induced drift using the steering wheel. Independent component analysis (ICA) applied to 30-channel EEG data separated the recorded EEG signals into a sum of maximally temporally independent components (ICs) for each of 30 subjects. Logarithmic spectra of resultant IC activities were then decomposed by principal component analysis, followed by ICA, to find spectrally fixed and temporally independent modulators (IM). Across subjects, the spectral ICA consistently found four performance-related independent modulators: delta, delta-theta, alpha, and beta modulators that multiplicatively affected the spectra of spatially distinct IC processes when the participants experienced waves of alternating alertness and drowsiness during long-hour simulated driving. The activation of the delta-theta modulator increased monotonically as subjects' task performances decreased. Furthermore, the time courses of the theta-beta modulator were highly correlated with concurrent changes in driving errors across subjects (r=0.77±0.13).
Publisher: IEEE
Date: 11-2017
Publisher: Elsevier BV
Date: 05-2020
Publisher: IEEE
Date: 2005
Publisher: IEEE
Date: 10-2016
DOI: 10.1109/SMC.2015.559
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2022
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Elsevier BV
Date: 05-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2021
Publisher: IEEE
Date: 07-2011
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2017
Publisher: Hindawi Limited
Date: 2013
DOI: 10.1155/2013/909721
Abstract: We expose the chaotic attractors of time-reversed nonlinear system, further implement its behavior on electronic circuit, and apply the pragmatical asymptotically stability theory to strictly prove that the adaptive synchronization of given master and slave systems with uncertain parameters can be achieved. In this paper, the variety chaotic motions of time-reversed Lorentz system are investigated through Lyapunov exponents, phase portraits, and bifurcation diagrams. For further applying the complex signal in secure communication and file encryption, we construct the circuit to show the similar chaotic signal of time-reversed Lorentz system. In addition, pragmatical asymptotically stability theorem and an assumption of equal probability for ergodic initial conditions (Ge et al., 1999, Ge and Yu, 2000, and Matsushima, 1972) are proposed to strictly prove that adaptive control can be accomplished successfully. The current scheme of adaptive control—by traditional Lyapunov stability theorem and Barbalat lemma, which are used to prove the error vector—approaches zero, as time approaches infinity. However, the core question—why the estimated or given parameters also approach to the uncertain parameters—remains without answer. By the new stability theory, those estimated parameters can be proved approaching the uncertain values strictly, and the simulation results are shown in this paper.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2009
Publisher: Hindawi Limited
Date: 2018
DOI: 10.1155/2018/5081258
Abstract: Electroencephalogram (EEG) signals are usually contaminated with various artifacts, such as signal associated with muscle activity, eye movement, and body motion, which have a noncerebral origin. The litude of such artifacts is larger than that of the electrical activity of the brain, so they mask the cortical signals of interest, resulting in biased analysis and interpretation. Several blind source separation methods have been developed to remove artifacts from the EEG recordings. However, the iterative process for measuring separation within multichannel recordings is computationally intractable. Moreover, manually excluding the artifact components requires a time-consuming offline process. This work proposes a real-time artifact removal algorithm that is based on canonical correlation analysis (CCA), feature extraction, and the Gaussian mixture model (GMM) to improve the quality of EEG signals. The CCA was used to decompose EEG signals into components followed by feature extraction to extract representative features and GMM to cluster these features into groups to recognize and remove artifacts. The feasibility of the proposed algorithm was demonstrated by effectively removing artifacts caused by blinks, head/body movement, and chewing from EEG recordings while preserving the temporal and spectral characteristics of the signals that are important to cognitive research.
Publisher: Elsevier BV
Date: 05-2000
Publisher: Springer Science and Business Media LLC
Date: 13-06-2023
DOI: 10.1007/S00521-022-07425-9
Abstract: Mental task classification (MTC), based on the electroencephalography (EEG) signals is a demanding brain–computer interface (BCI). It is independent of all types of muscular activity. MTC-based BCI systems are capable to identify cognitive activity of human. The success of BCI system depends upon the efficient feature representation from raw EEG signals for classification of mental activities. This paper mainly presents on a novel feature representation (formation of most informative features) of the EEG signal for the both, binary as well as multi MTC, using a combination of some statistical, uncertainty and memory- based coefficient. In this work, the feature formation is carried out in the two stages. In the first stage, the signal is split into different oscillatory functions with the help of three well-known empirical mode decomposition (EMD) algorithms, and a new set of eight parameters (features) are calculated from the oscillatory function in the second stage of feature vector construction. Support vector machine (SVM) is used to classify the feature vectors obtained corresponding to the different mental tasks. This study consists the problem formulation of two variants of MTC two-class and multi-class MTC. The suggested scheme outperforms the existing work for the both types of mental tasks classification.
Publisher: Springer International Publishing
Date: 2022
Publisher: Elsevier BV
Date: 05-2014
DOI: 10.1016/J.NEUROIMAGE.2014.01.015
Abstract: This study investigated the effects of kinesthetic stimuli on brain activities during a sustained-attention task in an immersive driving simulator. Tonic and phasic brain responses on multiple timescales were analyzed using time-frequency analysis of electroencephalographic (EEG) sources identified by independent component analysis (ICA). Sorting EEG spectra with respect to reaction times (RT) to randomly introduced lane-departure events revealed distinct effects of kinesthetic stimuli on the brain under different performance levels. Experimental results indicated that EEG spectral dynamics highly correlated with performance lapses when driving involved kinesthetic feedback. Furthermore, in the realistic environment involving both visual and kinesthetic feedback, a transitive relationship of power spectra between optimal-, suboptimal-, and poor-performance groups was found predominately across most of the independent components. In contrast to the static environment with visual input only, kinesthetic feedback reduced theta-power augmentation in the central and frontal components when preparing for action and error monitoring, while strengthening alpha suppression in the central component while steering the wheel. In terms of behavior, subjects tended to have a short response time to process unexpected events with the assistance of kinesthesia, yet only when their performance was optimal. Decrease in attentional demand, facilitated by kinesthetic feedback, eventually significantly increased the reaction time in the suboptimal-performance state. Neurophysiological evidence of mutual relationships between behavioral performance and neurocognition in complex task paradigms and experimental environments, presented in this study, might elucidate our understanding of distributed brain dynamics, supporting natural human cognition and complex coordinated, multi-joint naturalistic behavior, and lead to improved understanding of brain-behavior relations in operating environments.
Publisher: Springer Science and Business Media LLC
Date: 06-07-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2000
DOI: 10.1109/3468.867865
Publisher: Elsevier BV
Date: 05-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-1995
DOI: 10.1109/91.388172
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2003
Abstract: The structure classification of proteins plays a very important role in bioinformatics, since the relationships and characteristics among those known proteins can be exploited to predict the structure of new proteins. The success of a classification system depends heavily on two things: the tools being used and the features considered. For the bioinformatics applications, the role of appropriate features has not been paid adequate importance. In this investigation we use three novel ideas for multiclass protein fold classification. First, we use the gating neural network, where each input node is associated with a gate. This network can select important features in an online manner when the learning goes on. At the beginning of the training, all gates are almost closed, i.e., no feature is allowed to enter the network. Through the training, gates corresponding to good features are completely opened while gates corresponding to bad features are closed more tightly, and some gates may be partially open. The second novel idea is to use a hierarchical learning architecture (HLA). The classifier in the first level of HLA classifies the protein features into four major classes: all alpha, all beta, alpha + beta, and alpha/beta. And in the next level we have another set of classifiers, which further classifies the protein features into 27 folds. The third novel idea is to induce the indirect coding features from the amino-acid composition sequence of proteins based on the N-gram concept. This provides us with more representative and discriminative new local features of protein sequences for multiclass protein fold classification. The proposed HLA with new indirect coding features increases the protein fold classification accuracy by about 12%. Moreover, the gating neural network is found to reduce the number of features drastically. Using only half of the original features selected by the gating neural network can reach comparable test accuracy as that using all the original features. The gating mechanism also helps us to get a better insight into the folding process of proteins. For ex le, tracking the evolution of different gates we can find which characteristics (features) of the data are more important for the folding process. And, of course, it also reduces the computation time.
Publisher: IEEE
Date: 10-2016
DOI: 10.1109/SMC.2015.561
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2014
Publisher: IEEE
Date: 07-2020
Publisher: IEEE
Date: 2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2012
Publisher: IEEE
Date: 05-2017
Publisher: MDPI AG
Date: 19-04-2023
DOI: 10.20944/PREPRINTS201904.0091.V5
Abstract: Equality and incomparability multi-label ranking have not been introduced to learning before. This paper proposes new native ranker neural network to address the problem of multi-label ranking including incomparable preference orders using a new activation and error functions and new architecture. Preference Neural Network PNN solves the multi-label ranking problem, where labels may have indifference preference orders or subgroups which are equally ranked. PNN is a nondeep, multiple-value neuron, single middle layer and one or more output layers network. PNN uses a novel positive smooth staircase (PSS) or smooth staircase (SS) activation function and represents preference orders and Spearman ranking correlation as objective functions. It is introduced in two types, Type A is traditional NN architecture and Type B uses expanding architecture by introducing new type of hidden neuron has multiple activation function in middle layer and duplicated output layers to reinforce the ranking by increasing the number of weights. PNN accepts single data instance as inputs and output neurons represent the number of labels and output value represents the preference value. PNN is evaluated using a new preference mining data set that contains repeated label values which have not experimented on before. SS and PS speed-up the learning and PNN outperforms five previously proposed methods for strict label ranking in terms of accurate results with high computational efficiency.
Publisher: MDPI AG
Date: 24-12-2021
DOI: 10.20944/PREPRINTS201904.0091.V4
Abstract: Equality and incomparability multi-label ranking have not been introduced to learning before. This paper proposes new native ranker neural network to address the problem of multi-label ranking including incomparable preference orders using a new activation and error functions and new architecture. Preference Neural Network PNN solves the multi-label ranking problem, where labels may have indifference preference orders or subgroups which are equally ranked. PNN is a nondeep, multiple-value neuron, single middle layer and one or more output layers network. PNN uses a novel positive smooth staircase (PSS) or smooth staircase (SS) activation function and represents preference orders and Spearman ranking correlation as objective functions. It is introduced in two types, Type A is traditional NN architecture and Type B uses expanding architecture by introducing new type of hidden neuron has multiple activation function in middle layer and duplicated output layers to reinforce the ranking by increasing the number of weights. PNN accepts single data instance as inputs and output neurons represent the number of labels and output value represents the preference value. PNN is evaluated using a new preference mining data set that contains repeated label values which have not experimented on before. SS and PS speed-up the learning and PNN outperforms five previously proposed methods for strict label ranking in terms of accurate results with high computational efficiency.
Publisher: IEEE
Date: 05-2014
Publisher: MDPI AG
Date: 07-06-2020
DOI: 10.20944/PREPRINTS201904.0091.V3
Abstract: Equality and incomparability multi-label ranking have not been introduced to learning before. This paper proposes new native ranker neural network to address the problem of multi-label ranking including incomparable preference orders using a new activation and error functions and new architecture. Preference Neural Network PNN solves the multi-label ranking problem, where labels may have indifference preference orders or subgroups which are equally ranked. PNN is a nondeep, multiple-value neuron, single middle layer and one or more output layers network. PNN uses a novel positive smooth staircase (PSS) or smooth staircase (SS) activation function and represents preference orders and Spearman ranking correlation as objective functions. It is introduced in two types, Type A is traditional NN architecture and Type B uses expanding architecture by introducing new type of hidden neuron has multiple activation function in middle layer and duplicated output layers to reinforce the ranking by increasing the number of weights. PNN accepts single data instance as inputs and output neurons represent the number of labels and output value represents the preference value. PNN is evaluated using a new preference mining data set that contains repeated label values which have not experimented on before. SS and PS speed-up the learning and PNN outperforms five previously proposed methods for strict label ranking in terms of accurate results with high computational efficiency.
Publisher: MDPI AG
Date: 05-06-2020
DOI: 10.20944/PREPRINTS201904.0091.V2
Abstract: Equality and incomparability multi-label ranking have not been introduced to learning before. This paper proposes new native ranker neural network to address the problem of multi-label ranking including incomparable preference orders using a new activation and error functions and new architecture. Preference Neural Network PNN solves the multi-label ranking problem, where labels may have indifference preference orders or subgroups which are equally ranked. PNN is a nondeep, multiple-value neuron, single middle layer and one or more output layers network. PNN uses a novel positive smooth staircase (PSS) or smooth staircase (SS) activation function and represents preference orders and Spearman ranking correlation as objective functions. It is introduced in two types, Type A is traditional NN architecture and Type B uses expanding architecture by introducing new type of hidden neuron has multiple activation function in middle layer and duplicated output layers to reinforce the ranking by increasing the number of weights. PNN accepts single data instance as inputs and output neurons represent the number of labels and output value represents the preference value. PNN is evaluated using a new preference mining data set that contains repeated label values which have not experimented on before. SS and PS speed-up the learning and PNN outperforms five previously proposed methods for strict label ranking in terms of accurate results with high computational efficiency.
Publisher: MDPI AG
Date: 08-04-2019
DOI: 10.20944/PREPRINTS201904.0091.V1
Abstract: This paper proposes a preference neural network (PNN) to address the problem of indifference preferences orders with new activation function. PNN also solves the Multi-label ranking problem, where labels may have indifference preference orders or subgroups are equally ranked. PNN follows a multi-layer feedforward architecture with fully connected neurons. Each neuron contains a novel smooth stairstep activation function based on the number of preference orders. PNN inputs represent data features and output neurons represent label indexes. The proposed PNN is evaluated using new preference mining dataset that contains repeated label values which have not experimented before. PNN outperforms five previously proposed methods for strict label ranking in terms of accurate results with high computational efficiency.
Publisher: IEEE
Date: 10-2006
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2000
DOI: 10.1109/3477.836376
Abstract: This paper proposes a TD (temporal difference) and GA (genetic algorithm)-based reinforcement (TDGAR) learning method and applies it to the control of a real magnetic bearing system. The TDGAR learning scheme is a new hybrid GA, which integrates the TD prediction method and the GA to perform the reinforcement learning task. The TDGAR learning system is composed of two integrated feedforward networks. One neural network acts as a critic network to guide the learning of the other network (the action network) which determines the outputs (actions) of the TDGAR learning system. The action network can be a normal neural network or a neural fuzzy network. Using the TD prediction method, the critic network can predict the external reinforcement signal and provide a more informative internal reinforcement signal to the action network. The action network uses the GA to adapt itself according to the internal reinforcement signal. The key concept of the TDGAR learning scheme is to formulate the internal reinforcement signal as the fitness function for the GA such that the GA can evaluate the candidate solutions (chromosomes) regularly, even during periods without external feedback from the environment. This enables the GA to proceed to new generations regularly without waiting for the arrival of the external reinforcement signal. This can usually accelerate the GA learning since a reinforcement signal may only be available at a time long after a sequence of actions has occurred in the reinforcement learning problem. The proposed TDGAR learning system has been used to control an active magnetic bearing (AMB) system in practice. A systematic design procedure is developed to achieve successful integration of all the subsystems including magnetic suspension, mechanical structure, and controller training. The results show that the TDGAR learning scheme can successfully find a neural controller or a neural fuzzy controller for a self-designed magnetic bearing system.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2000
DOI: 10.1109/3477.836377
Abstract: An efficient genetic reinforcement learning algorithm for designing fuzzy controllers is proposed in this paper. The genetic algorithm (GA) adopted in this paper is based upon symbiotic evolution which, when applied to fuzzy controller design, complements the local mapping property of a fuzzy rule. Using this Symbiotic-Evolution-based Fuzzy Controller (SEFC) design method, the number of control trials, as well as consumed CPU time, are considerably reduced when compared to traditional GA-based fuzzy controller design methods and other types of genetic reinforcement learning schemes. Moreover, unlike traditional fuzzy controllers, which partition the input space into a grid, SEFC partitions the input space in a flexible way, thus creating fewer fuzzy rules. In SEFC, different types of fuzzy rules whose consequent parts are singletons, fuzzy sets, or linear equations (TSK-type fuzzy rules) are allowed. Further, the free parameters (e.g., centers and widths of membership functions) and fuzzy rules are all tuned automatically. For the TSK-type fuzzy rule especially, which put the proposed learning algorithm in use, only the significant input variables are selected to participate in the consequent of a rule. The proposed SEFC design method has been applied to different simulated control problems, including the cart-pole balancing system, a magnetic levitation system, and a water bath temperature control system. The proposed SEFC has been verified to be efficient and superior from these control problems, and from comparisons with some traditional GA-based fuzzy systems.
Publisher: Uppsala Medical Society
Date: 03-04-2018
Publisher: Elsevier BV
Date: 09-2017
Publisher: Springer Singapore
Date: 2021
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11552413_124
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: IEEE
Date: 04-2013
Publisher: IEEE
Date: 04-2013
Publisher: IEEE
Date: 1996
Publisher: Frontiers Media SA
Date: 27-03-2018
Publisher: ACM
Date: 12-11-2019
Publisher: IEEE
Date: 08-2013
Publisher: IEEE
Date: 04-2011
Publisher: Walter de Gruyter GmbH
Date: 17-12-2017
Abstract: A novel data knowledge representation with the combination of structure learning ability of preprocessed collaborative fuzzy clustering and fuzzy expert knowledge of Takagi- Sugeno-Kang type model is presented in this paper. The proposed method ides a huge dataset into two or more subsets of dataset. The subsets of dataset interact with each other through a collaborative mechanism in order to find some similar properties within each-other. The proposed method is useful in dealing with big data issues since it ides a huge dataset into subsets of dataset and finds common features among the subsets. The salient feature of the proposed method is that it uses a small subset of dataset and some common features instead of using the entire dataset and all the features. Before interactions among subsets of the dataset, the proposed method applies a mapping technique for granules of data and centroid of clusters. The proposed method uses information of only half or less/more than the half of the data patterns for the training process, and it provides an accurate and robust model, whereas the other existing methods use the entire information of the data patterns. Simulation results show the proposed method performs better than existing methods on some benchmark problems.
Publisher: MDPI AG
Date: 07-11-2022
DOI: 10.3390/APP122111265
Abstract: Organised attacks on a computer system to test existing defences, i.e., penetration testing, have been used extensively to evaluate network security. However, penetration testing is a time-consuming process. Additionally, establishing a strategy that resembles a real cyber-attack typically requires in-depth knowledge of the cybersecurity domain. This paper presents a novel architecture, named deep cascaded reinforcement learning agents, or CRLA, that addresses large discrete action spaces in an autonomous penetration testing simulator, where the number of actions exponentially increases with the complexity of the designed cybersecurity network. Employing an algebraic action decomposition strategy, CRLA is shown to find the optimal attack policy in scenarios with large action spaces faster and more stably than a conventional deep Q-learning agent, which is commonly used as a method for applying artificial intelligence to autonomous penetration testing.
Publisher: Inderscience Publishers
Date: 2013
Publisher: IEEE
Date: 11-2019
Publisher: Elsevier BV
Date: 02-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2020
Publisher: World Scientific Pub Co Pte Lt
Date: 31-07-2014
DOI: 10.1142/S012906571450021X
Abstract: For many applications, to reduce the processing time and the cost of decision making, we need to reduce the number of sensors, where each sensor produces a set of features. This sensor selection problem is a generalized feature selection problem. Here, we first present a sensor (group-feature) selection scheme based on Multi-Layered Perceptron Networks. This scheme sometimes selects redundant groups of features. So, we propose a selection scheme which can control the level of redundancy between the selected groups. The idea is general and can be used with any learning scheme. We have demonstrated the effectiveness of our scheme on several data sets. In this context, we define different measures of sensor dependency (dependency between groups of features). We have also presented an alternative learning scheme which is more effective than our old scheme. The proposed scheme is also adapted to radial basis function (RBS) network. The advantages of our scheme are threefold. It looks at all the groups together and hence can exploit nonlinear interaction between groups, if any. Our scheme can simultaneously select useful groups as well as learn the underlying system. The level of redundancy among groups can also be controlled.
Publisher: Royal Society of Chemistry (RSC)
Date: 2017
DOI: 10.1039/C7RA04657J
Abstract: Surface modification of Ag nanoparticles with PAA–PVP complex was conducted and successfully improved the dispersion of Ag nanoparticles in PDMS.
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: IEEE
Date: 07-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2023
Publisher: IEEE
Date: 02-05-2022
Publisher: Acoustical Society of America (ASA)
Date: 2000
DOI: 10.1121/1.428306
Abstract: An on-line fault detection and isolation technique is proposed for the diagnosis of rotating machinery. The architecture of the system consists of a feature generation module and a fault inference module. Lateral vibration data are used for calculating the system features. Both continuous-time and discrete-time parameter estimation algorithms are employed for generating the features. A neural fuzzy network is exploited for intelligent inference of faults based on the extracted features. The proposed method is implemented on a digital signal processor. Experiments carried out for a rotor kit and a centrifugal fan indicate the potential of the proposed techniques in predictive maintenance.
Publisher: Elsevier BV
Date: 09-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 1991
DOI: 10.1109/12.106218
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: Springer Science and Business Media LLC
Date: 24-05-2010
DOI: 10.1155/2010/468329
Publisher: IOP Publishing
Date: 10-2022
Abstract: This Perspective offers a concise overview of the current, state-of-the-art, neural sensors for brain-machine interfaces, with particular attention towards brain-controlled robotics. We first describe current approaches, decoding models and associated choice of common paradigms, and their relation to the position and requirements of the neural sensors. While implanted intracortical sensors offer unparalleled spatial, temporal and frequency resolution, the risks related to surgery and post-surgery complications pose a significant barrier to deployment beyond severely disabled in iduals. For less critical and larger scale applications, we emphasize the need to further develop dry scalp electroencephalography (EEG) sensors as non-invasive probes with high sensitivity, accuracy, comfort and robustness for prolonged and repeated use. In particular, as many of the employed paradigms require placing EEG sensors in hairy areas of the scalp, ensuring the aforementioned requirements becomes particularly challenging. Nevertheless, neural sensing technologies in this area are accelerating thanks to the advancement of miniaturised technologies and the engineering of novel biocompatible nanomaterials. The development of novel multifunctional nanomaterials is also expected to enable the integration of redundancy by probing the same type of information through different mechanisms for increased accuracy, as well as the integration of complementary and synergetic functions that could range from the monitoring of physiological states to incorporating optical imaging.
Publisher: Hindawi Limited
Date: 2013
DOI: 10.1155/2013/875965
Abstract: Fuzzy electronic circuit (FEC) is firstly introduced, which is implementing Takagi-Sugeno (T-S) fuzzy chaotic systems on electronic circuit. In the research field of secure communications, the original source should be blended with other complex signals. Chaotic signals are one of the good sources to be applied to encrypt high confidential signals, because of its high complexity, sensitiveness of initial conditions, and unpredictability. Consequently, generating chaotic signals on electronic circuit to produce real electrical signals applied to secure communications is an exceedingly important issue. However, nonlinear systems are always composed of many complex equations and are hard to realize on electronic circuits. Takagi-Sugeno (T-S) fuzzy model is a powerful tool, which is described by fuzzy IF-THEN rules to express the local dynamics of each fuzzy rule by a linear system model. Accordingly, in this paper, we produce the chaotic signals via electronic circuits through T-S fuzzy model and the numerical simulation results provided by MATLAB are also proposed for comparison. T-S fuzzy chaotic Lorenz and Chen-Lee systems are used for ex les and are given to demonstrate the effectiveness of the proposed electronic circuit.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: MDPI AG
Date: 27-10-2020
DOI: 10.3390/SU12218899
Abstract: Fire is one of the mutable hazards that damage properties and destroy forests. Many researchers are involved in early warning systems, which considerably minimize the consequences of fire damage. However, many existing image-based fire detection systems can perform well in a particular field. A general framework is proposed in this paper which works on realistic conditions. This approach filters out image blocks based on thresholds of different temporal and spatial features, starting with iding the image into blocks and extraction of flames blocks from image foreground and background, and candidates blocks are analyzed to identify local features of color, source immobility, and flame flickering. Each local feature filter resolves different false-positive fire cases. Filtered blocks are further analyzed by global analysis to extract flame texture and flame reflection in surrounding blocks. Sequences of successful detections are buffered by a decision alarm system to reduce errors due to external camera influences. Research algorithms have low computation time. Through a sequence of experiments, the result is consistent with the empirical evidence and shows that the detection rate of the proposed system exceeds previous studies and reduces false alarm rates under various environments.
Publisher: Springer Science and Business Media LLC
Date: 20-11-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2016
Publisher: IEEE
Date: 06-2008
Publisher: IEEE
Date: 09-2015
Publisher: Elsevier BV
Date: 12-1998
DOI: 10.1016/S0893-6080(98)00091-4
Abstract: This article proposes a new second-order learning algorithm for training the multilayer perceptron (MLP) networks. The proposed algorithm is a revised Newton's method. A forward-backward propagation scheme is first proposed for network computation of the Hessian matrix, H, of the output error function of the MLP. A block Hessian matrix, H(b), is then defined to approximate and simplify H. Several lemmas and theorems are proved to uncover the important properties of H and H(b), and verify the good approximation of H(b) to H H(b) preserves the major properties of H. The theoretic analysis leads to the development of an efficient way for computing the inverse of H(b) recursively. In the proposed second-order learning algorithm, the least squares estimation technique is adopted to further lessen the local minimum problems. The proposed algorithm overcomes not only the drawbacks of the standard backpropagation algorithm (i.e. slow asymptotic convergence rate, bad controllability of convergence accuracy, local minimum problems, and high sensitivity to learning constant), but also the shortcomings of normal Newton's method used on the MLP, such as the lack of network implementation of H, ill representability of the diagonal terms of H, the heavy computation load of the inverse of H, and the requirement of a good initial estimate of the solution (weights). Several ex le problems are used to demonstrate the efficiency of the proposed learning algorithm. Extensive performance (convergence rate and accuracy) comparisons of the proposed algorithm with other learning schemes (including the standard backpropagation algorithm) are also made.
Publisher: Elsevier BV
Date: 11-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2009
Publisher: Springer Science and Business Media LLC
Date: 23-05-2010
DOI: 10.1155/2010/983581
Publisher: Elsevier BV
Date: 05-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2001
DOI: 10.1109/89.902275
Publisher: Elsevier BV
Date: 04-2000
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2002
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2019
Publisher: IEEE
Date: 10-2012
Publisher: IEEE
Date: 2006
Publisher: Elsevier BV
Date: 05-2013
Publisher: SAGE Publications
Date: 06-2009
DOI: 10.2466/PMS.108.3.825-835
Abstract: Drivers' fatigue contributes to traffic accidents, so drivers must maintain adequate alertness. The effectiveness of audio alarms in maintaining driving performance and characteristics of alarms was studied in a virtural reality-based driving environment. Response time to the car's drifting was measured under seven conditions: with no warnings and with continuous warning tones (500 Hz, 1750 Hz, and 3000 Hz), and with tone bursts at 500 Hz, 1750 Hz, and 3000 Hz. Analyses showed the audio warning signals significantly improved driving. Further, the tones' spectral characteristics significantly influenced the effectiveness of the warning.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2005
Publisher: Elsevier BV
Date: 07-2018
Publisher: Springer International Publishing
Date: 2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2021
Publisher: Cold Spring Harbor Laboratory
Date: 02-12-2020
DOI: 10.1101/2020.12.01.406124
Abstract: Spatial navigation is a complex cognitive process based on multiple senses that are integrated and processed by a wide network of brain areas. Previous studies have revealed the retrosplenial complex (RSC) to be modulated in a task-related manner during navigation. However, these studies restricted participants’ movement to stationary setups, which might have impacted heading computations due to the absence of vestibular and proprioceptive inputs. Here, we investigated neural dynamics of RSC in an active spatial navigation task where participants actively ambulated from one location to several other points while the position of a landmark and the starting location were updated. The results revealed theta power in the RSC to be pronounced during heading changes but not during translational movements, indicating that physical rotations induce human RSC theta activity. This finding provides a potential evidence of head-direction computation in RSC in healthy humans during active spatial navigation.
Publisher: World Scientific Pub Co Pte Lt
Date: 11-2002
DOI: 10.1142/S0218001402002076
Abstract: This paper addresses the problem of speech segmentation and enhancement in the presence of noise. We first propose a new word boundary detection algorithm by using a neural fuzzy network (called ATF-based SONFIN algorithm) for identifying islands of word signals in fixed noise-level environment. We further propose a new RTF-based RSONFIN algorithm where the background noise level varies during the procedure of recording. The adaptive time-frequency (ATF) and refined time-frequency (RTF) parameters extend the TF parameter from single band to multiband spectrum analysis, and help to make the distinction of speech and noise signals clear. The ATF and RTF parameters can extract useful frequency information by adaptively choosing proper bands of the mel-scale frequency bank. Due to the self-learning ability of SONFIN and RSONFIN, the proposed algorithms avoid the need of empirically determining thresholds and ambiguous rules. The RTF-based RSONFIN algorithm can also find the variation of the background noise level and detect correct word boundaries in the condition of variable background noise level by processing the temporal relations. Our experimental results show that both in the fixed and variable noise-level environment, the algorithms that we proposed achieved higher recognition rate than several commonly used word boundary detection algorithms and reduced the recognition error rate due to endpoint detection.
Publisher: Elsevier BV
Date: 15-08-2010
DOI: 10.1016/J.NEUROIMAGE.2010.03.065
Abstract: The present study reported the development of a novel functional photoacoustic microscopy (fPAM) system for investigating hemodynamic changes in rat cortical vessels associated with electrical forepaw stimulation. Imaging of blood optical absorption by fPAM at multiple appropriately-selected and distinct wavelengths can be used to probe changes in total hemoglobin concentration (HbT, i.e., cerebral blood volume [CBV]) and hemoglobin oxygen saturation (SO(2)). Changes in CBV were measured by images acquired at a wavelength of 570nm (lambda(570)), an isosbestic point of the molar extinction spectra of oxy- and deoxy-hemoglobin, whereas SO(2) changes were sensed by pixel-wise normalization of images acquired at lambda(560) or lambda(600) to those at lambda(570). We demonstrated the capacity of the fPAM system to image and quantify significant contralateral changes in both SO(2) and CBV driven by electrical forepaw stimulation. The fPAM system complements existing imaging techniques, with the potential to serve as a favorable tool for explicitly studying brain hemodynamics in animal models.
Publisher: JMIR Publications Inc.
Date: 08-10-2020
DOI: 10.2196/22821
Abstract: Chronic pain is a global health problem, affecting around 1 in 5 in iduals in the general population. The understanding of the key role of functional brain alterations in the generation of chronic pain has led researchers to focus on pain treatments that target brain activity. Electroencephalographic neurofeedback attempts to modulate the power of maladaptive electroencephalography frequency powers to decrease chronic pain. Although several studies have provided promising evidence, the effect of electroencephalographic neurofeedback on chronic pain is uncertain. This systematic review aims to synthesize the evidence from randomized controlled trials to evaluate the analgesic effect of electroencephalographic neurofeedback. In addition, we will synthesize the findings of nonrandomized studies in a narrative review. We will apply the search strategy in 5 electronic databases (Cochrane Central Register of Controlled Trials, MEDLINE, EMBASE, PsycInfo, and CINAHL) for published studies and in clinical trial registries for completed unpublished studies. We will include studies that used electroencephalographic neurofeedback as an intervention for people with chronic pain. Risk-of-bias tools will be used to assess methodological quality of the included studies. We will include randomized controlled trials if they have compared electroencephalographic neurofeedback with any other intervention or placebo control. The data from randomized controlled trials will be aggregated to perform a meta-analysis for quantitative synthesis. The primary outcome measure is pain intensity assessed by self-report scales. Secondary outcome measures include depressive symptoms, anxiety symptoms, and sleep quality measured by self-reported questionnaires. We will investigate the studies for additional outcomes addressing adverse effects and resting-state electroencephalography analysis. Additionally, all types of nonrandomized studies will be included for a narrative synthesis. The intended and unintended effects of nonrandomized studies will be extracted and summarized in a descriptive table. Ethics approval is not required for a systematic review, as there will be no patient involvement. The search for this systematic review commenced in July 2020, and we expect to publish the findings in early 2021. This systematic review will provide recommendations for researchers and health professionals, as well as people with chronic pain, about the evidence for the analgesic effect of electroencephalographic neurofeedback. International Prospective Register of Systematic Reviews (PROSPERO) CRD42020177608 www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=177608 PRR1-10.2196/22821
Publisher: Elsevier BV
Date: 02-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: IEEE
Date: 10-2017
Publisher: Scientific Research Publishing, Inc.
Date: 2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2022
Publisher: Springer Science and Business Media LLC
Date: 11-08-2023
Publisher: IEEE
Date: 11-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: IEEE
Date: 10-2017
Publisher: IEEE
Date: 11-2010
Publisher: Elsevier BV
Date: 04-2002
Publisher: IEEE
Date: 1999
Publisher: ACM
Date: 02-05-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 1998
DOI: 10.1109/91.660811
Publisher: Springer London
Date: 2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2015
Publisher: IEEE
Date: 08-2015
Publisher: Springer International Publishing
Date: 2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2003
Publisher: IEEE
Date: 12-2006
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2001
DOI: 10.1109/91.917120
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: IEEE
Date: 08-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 1991
DOI: 10.1109/21.120051
Publisher: IEEE
Date: 10-2017
Publisher: American Chemical Society (ACS)
Date: 16-03-2023
Publisher: IEEE
Date: 10-2017
Publisher: Informa UK Limited
Date: 03-1998
Publisher: IEEE
Date: 03-11-2021
Publisher: American Society of Civil Engineers (ASCE)
Date: 09-1994
Publisher: Springer Science and Business Media LLC
Date: 06-07-2010
DOI: 10.1155/2010/296598
Publisher: IEEE
Date: 12-2020
Publisher: Elsevier BV
Date: 04-2014
Publisher: Elsevier BV
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2010
Publisher: IEEE
Date: 1994
Publisher: Elsevier
Date: 1999
Publisher: IEEE
Date: 11-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 1998
DOI: 10.1109/91.660805
Publisher: Springer Science and Business Media LLC
Date: 11-2013
Publisher: IEEE
Date: 10-08-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 1997
DOI: 10.1109/91.649900
Publisher: IOP Publishing
Date: 12-2021
Abstract: Objective . Brain–machine interfaces are key components for the development of hands-free, brain-controlled devices. Electroencephalogram (EEG) electrodes are particularly attractive for harvesting the neural signals in a non-invasive fashion. Approach. Here, we explore the use of epitaxial graphene (EG) grown on silicon carbide on silicon for detecting the EEG signals with high sensitivity. Main results and significance. This dry and non-invasive approach exhibits a markedly improved skin contact impedance when benchmarked to commercial dry electrodes, as well as superior robustness, allowing prolonged and repeated use also in a highly saline environment. In addition, we report the newly observed phenomenon of surface conditioning of the EG electrodes. The prolonged contact of the EG with the skin electrolytes functionalize the grain boundaries of the graphene, leading to the formation of a thin surface film of water through physisorption and consequently reducing its contact impedance more than three-fold. This effect is primed in highly saline environments, and could be also further tailored as pre-conditioning to enhance the performance and reliability of the EG sensors.
Publisher: IEEE
Date: 07-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2012
Publisher: IEEE
Date: 08-2011
Publisher: IEEE
Date: 07-2016
Publisher: Frontiers Media SA
Date: 19-06-2014
Publisher: Association for Research in Vision and Ophthalmology (ARVO)
Date: 21-03-2010
DOI: 10.1167/9.8.1132
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2023
Publisher: MDPI AG
Date: 16-05-2021
DOI: 10.3390/S21103461
Abstract: Recently, there has been an increase in the production of devices to monitor mental health and stress as means for expediting detection, and subsequent management of these conditions. The objective of this review is to identify and critically appraise the most recent smart devices and wearable technologies used to identify depression, anxiety, and stress, and the physiological process(es) linked to their detection. The MEDLINE, CINAHL, Cochrane Central, and PsycINFO databases were used to identify studies which utilised smart devices and wearable technologies to detect or monitor anxiety, depression, or stress. The included articles that assessed stress and anxiety unanimously used heart rate variability (HRV) parameters for detection of anxiety and stress, with the latter better detected by HRV and electroencephalogram (EGG) together. Electrodermal activity was used in recent studies, with high accuracy for stress detection however, with questionable reliability. Depression was found to be largely detected using specific EEG signatures however, devices detecting depression using EEG are not currently available on the market. This systematic review highlights that average heart rate used by many commercially available smart devices is not as accurate in the detection of stress and anxiety compared with heart rate variability, electrodermal activity, and possibly respiratory rate.
Publisher: Public Library of Science (PLoS)
Date: 04-12-2019
Publisher: Cold Spring Harbor Laboratory
Date: 03-2021
DOI: 10.1101/2021.02.28.433279
Abstract: Recent research into navigation strategy of different spatial reference frame proclivities (RFPs) has revealed that the parietal cortex plays an important role in processing allocentric information to provide a translation function between egocentric and allocentric spatial reference frames. However, most studies merely focused on a passive experimental environment, which is not truly representative of our daily spatial learning/navigation tasks. This study investigated the factor associated with brain dynamics that causes people to switch their preferred spatial strategy in different environments in virtual reality (VR) based active navigation task to bridge the gap. High-resolution electroencephalography (EEG) signals were recorded to monitor spectral perturbations on transitions between egocentric and allocentric frames during a path integration task. Our brain dynamics results showed navigation involved areas including the parietal cortex with modulation in the alpha band, the occipital cortex with beta and low gamma band perturbations, and the frontal cortex with theta perturbation. Differences were found between two different turning-angle paths in the alpha band in parietal cluster event-related spectral perturbations (ERSPs). In small turning-angle paths, allocentric participants showed stronger alpha desynchronization than egocentric participants in large turning-angle paths, participants for two reference frames had a smaller difference in the alpha frequency band. Behavior results of homing errors also corresponded to brain dynamic results, indicating that a larger angle path caused the allocentric to have a higher tendency to become egocentric navigators in the active navigation environment.
Publisher: Elsevier BV
Date: 11-2023
Publisher: CRC Press
Date: 04-05-2023
Publisher: IEEE
Date: 2022
Publisher: IEEE
Date: 10-2014
Publisher: Hindawi Limited
Date: 10-06-2021
DOI: 10.1155/2021/9954669
Abstract: For emergency or intensive-care units (ICUs), patients with unclear consciousness or unstable hemodynamics often require aggressive monitoring by multiple monitors. Complicated pipelines or lines increase the burden on patients and inconvenience for medical personnel. Currently, many commercial devices provide related functionalities. However, most devices measure only one biological signal, which can increase the budget for users and cause difficulty in remote integration. In this study, we develop a wearable device that integrates electrocardiography (ECG), electroencephalography (EEG), and blood oxygen machines for medical applications with the hope that it can be applied in the future. We develop an integrated multiple-biosignal recording system based on a modular design. The developed system monitors and records EEG, ECG, and peripheral oxygen saturation (SpO2) signals for health purposes simultaneously in a single setting. We use a logic level converter to connect the developed EEG module (BR8), ECG module, and SpO2 module to a microcontroller (Arduino). The modular data are then smoothly encoded and decoded through consistent overhead byte stuffing (COBS). This developed system has passed simulation tests and exhibited proper functioning of all modules and subsystems. In the future, the functionalities of the proposed system can be expanded with additional modules to support various emergency or ICU applications.
Publisher: IEEE
Date: 08-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2006
Publisher: IEEE
Date: 05-2018
Publisher: EDP Sciences
Date: 2018
DOI: 10.1051/MATECCONF/201820105004
Abstract: In this study, two intelligent classifiers, the AdaBoost-based incremental functional neural fuzzy classifier (AIFNFC) and the AdaBoost-based fixed functional neural fuzzy classifier (AFFNFC), are proposed for solving the classification problems. The AIFNFC approach will increase the amount of functional neural fuzzy classifiers based on the corresponding error during the training phase while the AFNFC approach is equipped with a fixed amount of functional neural fuzzy classifiers. Then, the weights of AdaBoost procedure are assigned for classifiers. The proposed methods are applied to different classification benchmarks. Results of this study demonstrate the effectiveness of the proposed AIFNFC and AFFNFC methods.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2002
Publisher: Elsevier BV
Date: 09-2001
Publisher: IEEE
Date: 08-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-1996
DOI: 10.1109/72.501728
Abstract: This paper proposes a reinforcement fuzzy adaptive learning control network (RFALCON), constructed by integrating two fuzzy adaptive learning control networks (FALCON), each of which has a feedforward multilayer network and is developed for the realization of a fuzzy controller. One FALCON performs as a critic network (fuzzy predictor), the other as an action network (fuzzy controller). Using temporal difference prediction, the critic network can predict the external reinforcement signal and provide a more informative internal reinforcement signal to the action network. The action network performs a stochastic exploratory algorithm to adapt itself according to the internal reinforcement signal. An ART-based reinforcement structure arameter-learning algorithm is developed for constructing the RFALCON dynamically. During the learning process, structure and parameter learning are performed simultaneously. RFALCON can construct a fuzzy control system through a reward enalty signal. It has two important features it reduces the combinatorial demands of system adaptive linearization, and it is highly autonomous.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2022
Publisher: IEEE
Date: 03-2023
Publisher: IEEE
Date: 11-2010
Publisher: IEEE
Date: 05-2013
Publisher: IEEE
Date: 1995
Publisher: Springer Berlin Heidelberg
Date: 2007
Publisher: Elsevier BV
Date: 10-2018
Publisher: Springer Berlin Heidelberg
Date: 2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2003
Publisher: World Scientific Pub Co Pte Lt
Date: 10-2002
DOI: 10.1142/S0219467802000780
Abstract: In this paper, a novel edge-oriented neural-network-based adaptive interpolation scheme for natural image is proposed. An image analysis module is used to classify pixels of the input image into non-oriented class and oriented class. The bilinear interpolation is used to interpolate the non-oriented regions and a neural network is proposed to interpolate the oriented regions. High-resolution digital images along with supervised learning algorithms can be used to automatically train the proposed neural network. Simulation results demonstrate that the proposed new resolution enhancement algorithm can produce higher visual quality of the interpolated image than the conventional interpolation methods.
Publisher: IEEE
Date: 08-2015
Publisher: IEEE
Date: 04-12-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2004
DOI: 10.1109/TSMCB.2003.810953
Abstract: A fast target maneuver detecting and highly accurate tracking technique using a neural fuzzy network based on Kalman filter is proposed in this paper. In the automatic target tracking system, there exists an important and difficult problem: how to detect the target maneuvers and fast response to avoid miss-tracking? The traditional maneuver detection algorithms, such as variable dimension filter (VDF) and input estimation (IE) etc., are computation intensive and difficult to implement in real time. To solve this problem, neural network algorithms have been issued recently. However, the normal neural networks such as backpropagation networks usually produce the extra problems of low convergence speed and/or large network size. Furthermore, the way to decide the network structure is heuristic. To overcome these defects and to make use of neural learning ability, a developed standard Kalman filter with a self-constructing neural fuzzy inference network (KF-SONFIN) algorithm for target tracking is presented in this paper. By generating possible target trajectories including maneuver information to train the SONFIN, the trained SONFIN can detect when the maneuver occurred, the magnitude of maneuver values and when the maneuver disappeared. Without having to change the structure of Kalman filter nor modeling the maneuvering target, this new algorithm, SONFIN, can always find itself an economic network size with a fast learning process. Simulation results show that the KF-SONFIN is superior to the traditional IE and VDF methods in estimation accuracy.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: Elsevier BV
Date: 03-1995
Publisher: Association for Computing Machinery (ACM)
Date: 07-2006
Publisher: EJournal Publishing
Date: 2013
Publisher: Frontiers Media SA
Date: 25-11-2021
Abstract: The K-means algorithm is a widely used clustering algorithm that offers simplicity and efficiency. However, the traditional K-means algorithm uses a random method to determine the initial cluster centers, which make clustering results prone to local optima and then result in worse clustering performance. In this research, we propose an adaptive initialization method for the K-means algorithm (AIMK) which can adapt to the various characteristics in different datasets and obtain better clustering performance with stable results. For larger or higher-dimensional datasets, we even leverage random s ling in AIMK (name as AIMK-RS) to reduce the time complexity. 22 real-world datasets were applied for performance comparisons. The experimental results show AIMK and AIMK-RS outperform the current initialization methods and several well-known clustering algorithms. Specifically, AIMK-RS can significantly reduce the time complexity to O ( n ). Moreover, we exploit AIMK to initialize K-medoids and spectral clustering, and better performance is also explored. The above results demonstrate superior performance and good scalability by AIMK or AIMK-RS. In the future, we would like to apply AIMK to more partition-based clustering algorithms to solve real-life practical problems.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2012
Publisher: MIT Press
Date: 08-2020
DOI: 10.1162/NECO_A_01293
Abstract: A driver's cognitive state of mental fatigue significantly affects his or her driving performance and more important, public safety. Previous studies have leveraged reaction time (RT) as the metric for mental fatigue and aim at estimating the exact value of RT using electroencephalogram (EEG) signals within a regression model. However, due to the easily corrupted and also nonsmooth properties of RTs during data collection, methods focusing on predicting the exact value of a noisy measurement, RT generally suffer from poor generalization performance. Considering that human RT is the reflection of brain dynamics preference (BDP) rather than a single regression output of EEG signals, we propose a novel channel-reliability-aware ranking (CArank) model for the multichannel ranking problem. CArank learns from BDPs using EEG data robustly and aims at preserving the ordering corresponding to RTs. In particular, we introduce a transition matrix to characterize the reliability of each channel used in the EEG data, which helps in learning with BDPs only from informative EEG channels. To handle large-scale EEG signals, we propose a stochastic-generalized expectation maximum (SGEM) algorithm to update CArank in an online fashion. Comprehensive empirical analysis on EEG signals from 40 participants shows that our CArank achieves substantial improvements in reliability while simultaneously detecting noisy or less informative EEG channels.
Publisher: MDPI AG
Date: 31-10-2016
DOI: 10.3390/S16111826
Publisher: IEEE
Date: 1998
Publisher: World Scientific Pub Co Pte Lt
Date: 08-1999
DOI: 10.1142/S0129065799000319
Abstract: Motion recognition has received increasing attention in recent years owing to heightened demand for computer vision in many domains, including the surveillance system, multimodal human computer interface, and traffic control system. Most conventional approaches classify the motion recognition task into partial feature extraction and time-domain recognition subtasks. However, the information of motion resides in the space-time domain instead of the time domain or space domain independently, implying that fusing the feature extraction and classification in the space and time domains into a single framework is preferred. Based on this notion, this work presents a novel Space-Time Delay Neural Network (STDNN) capable of handling the space-time dynamic information for motion recognition. The STDNN is unified structure, in which the low-level spatiotemporal feature extraction and high-level space-time-domain recognition are fused. The proposed network possesses the spatiotemporal shift-invariant recognition ability that is inherited from the time delay neural network (TDNN) and space displacement neural network (SDNN), where TDNN and SDNN are good at temporal and spatial shift-invariant recognition, respectively. In contrast to multilayer perceptron (MLP), TDNN, and SDNN, STDNN is constructed by vector-type nodes and matrix-type links such that the spatiotemporal information can be accurately represented in a neural network. Also evaluated herein is the performance of the proposed STDNN via two experiments. The moving Arabic numerals (MAN) experiment simulates the object's free movement in the space-time domain on image sequences. According to these results, STDNN possesses a good generalization ability with respect to the spatiotemporal shift-invariant recognition. In the lipreading experiment, STDNN recognizes the lip motions based on the inputs of real image sequences. This observation confirms that STDNN yields a better performance than the existing TDNN-based system, particularly in terms of the generalization ability. In addition to the lipreading application, the STDNN can be applied to other problems since no domain-dependent knowledge is used in the experiment.
Publisher: IEEE
Date: 10-2007
Publisher: IEEE
Date: 07-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2010
Publisher: World Scientific Pub Co Pte Lt
Date: 08-2019
DOI: 10.1142/S0129065719500187
Abstract: Fatigue is one problem with driving as it can lead to difficulties with sustaining attention, behavioral lapses, and a tendency to ignore vital information or operations. In this research, we explore multimodal physiological phenomena in response to driving fatigue through simultaneous functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) recordings with the aim of investigating the relationships between hemodynamic and electrical features and driving performance. Sixteen subjects participated in an event-related lane-deviation driving task while measuring their brain dynamics through fNIRS and EEGs. Three performance groups, classified as Optimal, Suboptimal, and Poor, were defined for comparison. From our analysis, we find that tonic variations occur before a deviation, and phasic variations occur afterward. The tonic results show an increased concentration of oxygenated hemoglobin (HbO 2 ) and power changes in the EEG theta, alpha, and beta bands. Both dynamics are significantly correlated with deteriorated driving performance. The phasic EEG results demonstrate event-related desynchronization associated with the onset of steering vehicle in all power bands. The concentration of phasic HbO 2 decreased as performance worsened. Further, the negative correlations between tonic EEG delta and alpha power and HbO 2 oscillations suggest that activations in HbO 2 are related to mental fatigue. In summary, combined hemodynamic and electrodynamic activities can provide complete knowledge of the brain’s responses as evidence of state changes during fatigue driving.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2023
Publisher: MDPI AG
Date: 28-11-2021
DOI: 10.3390/APP112311268
Abstract: A new online multi-class learning algorithm is proposed with three main characteristics. First, in order to make the feature pool fitter for the pattern pool, the adaptive feature pool is proposed to dynamically combine the three general features, Haar-like, Histogram of Oriented Gradient (HOG), and Local Binary Patterns (LBP). Second, the external model is integrated into the proposed model without re-training to enhance the efficacy of the model. Third, a new multi-class learning and updating mechanism are proposed that help to find unsuitable decisions and adjust them automatically. The performance of the proposed model is validated with multi-class detection and online learning system. The proposed model achieves a better score than other non-deep learning algorithms used in public pedestrian and multi-class databases. The multi-class databases contain data for pedestrians, faces, vehicles, motorcycles, bicycles, and aircraft.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2022
Publisher: IEEE
Date: 2004
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 10-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-1996
DOI: 10.1109/3477.537316
Abstract: A neural fuzzy system learning with fuzzy training data (fuzzy if-then rules) is proposed in this paper. This system is able to process and learn numerical information as well as linguistic information. At first, we propose a five-layered neural network for the connectionist realization of a fuzzy inference system. The connectionist structure can house fuzzy logic rules and membership functions for fuzzy inference. We use alpha-level sets of fuzzy numbers to represent linguistic information. The inputs, outputs, and weights of the proposed network can be fuzzy numbers of any shape. Furthermore, they can be hybrid of fuzzy numbers and numerical numbers through the use of fuzzy singletons. Based on interval arithmetics, a fuzzy supervised learning algorithm is developed for the proposed system. It extends the normal supervised learning techniques to the learning problems where only linguistic teaching signals are available. The fuzzy supervised learning scheme can train the proposed system with desired fuzzy input-output pairs which are fuzzy numbers instead of the normal numerical values. With fuzzy supervised learning, the proposed system can be used for rule base concentration to reduce the number of rules in a fuzzy rule base. Simulation results are presented to illustrate the performance and applicability of the proposed system.
Publisher: Springer International Publishing
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2022
Publisher: IEEE
Date: 2006
Publisher: Frontiers Media SA
Date: 06-08-2021
DOI: 10.3389/FCTEG.2021.722092
Abstract: In formation control, a robot (or an agent) learns to align itself in a particular spatial alignment. However, in a few scenarios, it is also vital to learn temporal alignment along with spatial alignment. An effective control system encompasses flexibility, precision, and timeliness. Existing reinforcement learning algorithms excel at learning to select an action given a state. However, executing an optimal action at an appropriate time remains challenging. Building a reinforcement learning agent which can learn an optimal time to act along with an optimal action can address this challenge. Neural networks in which timing relies on dynamic changes in the activity of population neurons have been shown to be a more effective representation of time. In this work, we trained a reinforcement learning agent to create its representation of time using a neural network with a population of recurrently connected nonlinear firing rate neurons. Trained using a reward-based recursive least square algorithm, the agent learned to produce a neural trajectory that peaks at the “time-to-act” thus, it learns “when” to act. A few control system applications also require the agent to temporally scale its action. We trained the agent so that it could temporally scale its action for different speed inputs. Furthermore, given one state, the agent could learn to plan multiple future actions, that is, multiple times to act without needing to observe a new state.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: IEEE
Date: 07-2014
Publisher: CRC Press
Date: 04-05-2023
Publisher: Springer Science and Business Media LLC
Date: 04-10-2012
Publisher: IEEE
Date: 2001
Publisher: IEEE
Date: 30-05-2021
Publisher: IEEE
Date: 06-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2005
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2017
Publisher: IEEE
Date: 07-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2019
Publisher: IEEE
Date: 05-2008
Publisher: IEEE
Date: 04-2007
Publisher: Elsevier BV
Date: 2018
Publisher: Springer Nature Singapore
Date: 2023
Publisher: Frontiers Media SA
Date: 09-02-2018
Publisher: IEEE
Date: 08-2015
Publisher: Frontiers Media SA
Date: 20-06-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2021
Publisher: Springer Science and Business Media LLC
Date: 04-10-2016
Publisher: Springer Science and Business Media LLC
Date: 28-01-2012
Abstract: A brain-computer interface (BCI) is a communication system that can help users interact with the outside environment by translating brain signals into machine commands. The use of electroencephalographic (EEG) signals has become the most common approach for a BCI because of their usability and strong reliability. Many EEG-based BCI devices have been developed with traditional wet- or micro-electro-mechanical-system (MEMS)-type EEG sensors. However, those traditional sensors have uncomfortable disadvantage and require conductive gel and skin preparation on the part of the user. Therefore, acquiring the EEG signals in a comfortable and convenient manner is an important factor that should be incorporated into a novel BCI device. In the present study, a wearable, wireless and portable EEG-based BCI device with dry foam-based EEG sensors was developed and was demonstrated using a gaming control application. The dry EEG sensors operated without conductive gel however, they were able to provide good conductivity and were able to acquire EEG signals effectively by adapting to irregular skin surfaces and by maintaining proper skin-sensor impedance on the forehead site. We have also demonstrated a real-time cognitive stage detection application of gaming control using the proposed portable device. The results of the present study indicate that using this portable EEG-based BCI device to conveniently and effectively control the outside world provides an approach for researching rehabilitation engineering.
Publisher: Elsevier BV
Date: 02-2019
Publisher: IEEE
Date: 05-2007
Publisher: Elsevier BV
Date: 02-2013
Publisher: IEEE
Date: 12-2015
DOI: 10.1109/SSCI.2015.13
Publisher: Springer Science and Business Media LLC
Date: 04-11-2008
DOI: 10.1155/2008/519480
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2021
Publisher: IEEE
Date: 28-06-2021
Publisher: IEEE
Date: 07-2014
Publisher: IEEE
Date: 12-2013
Publisher: IEEE
Date: 04-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2005
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2005
Publisher: Springer Science and Business Media LLC
Date: 18-02-2011
Abstract: Driver distraction is a significant cause of traffic accidents. The aim of this study is to investigate Electroencephalography (EEG) dynamics in relation to distraction during driving. To study human cognition under a specific driving task, simulated real driving using virtual reality (VR)-based simulation and designed dual-task events are built, which include unexpected car deviations and mathematics questions. We designed five cases with different stimulus onset asynchrony (SOA) to investigate the distraction effects between the deviations and equations. The EEG channel signals are first converted into separated brain sources by independent component analysis (ICA). Then, event-related spectral perturbation (ERSP) changes of the EEG power spectrum are used to evaluate brain dynamics in time-frequency domains. Power increases in the theta and beta bands are observed in relation with distraction effects in the frontal cortex. In the motor area, alpha and beta power suppressions are also observed. All of the above results are consistently observed across 15 subjects. Additionally, further analysis demonstrates that response time and multiple cortical EEG power both changed significantly with different SOA. This study suggests that theta power increases in the frontal area is related to driver distraction and represents the strength of distraction in real-life situations.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2023
Publisher: IEEE
Date: 07-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2006
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-1999
DOI: 10.1109/72.774236
Abstract: This paper proposes a TD (temporal difference) and GA (genetic algorithm) based reinforcement (TDGAR) neural learning scheme for controlling chaotic dynamical systems based on the technique of small perturbations. The TDGAR learning scheme is a new hybrid GA, which integrates the TD prediction method and the GA to fulfill the reinforcement learning task. Structurely, the TDGAR learning system is composed of two integrated feedforward networks. One neural network acts as a critic network for helping the learning of the other network, the action network, which determines the outputs (actions) of the TDGAR learning system. Using the TD prediction method, the critic network can predict the external reinforcement signal and provide a more informative internal reinforcement signal to the action network. The action network uses the GA to adapt itself according to the internal reinforcement signal. This can usually accelerate the GA learning since an external reinforcement signal may only be available at a time long after a sequence of actions have occurred in the reinforcement learning problems. By defining a simple external reinforcement signal, the TDGAR learning system can learn to produce a series of small perturbations to convert chaotic oscillations of a chaotic system into desired regular ones with a periodic behavior. The proposed method is an adaptive search for the optimum control technique. Computer simulations on controlling two chaotic systems, i.e., the Hénon map and the logistic map, have been conducted to illustrate the performance of the proposed method.
Publisher: JMIR Publications Inc.
Date: 29-09-2020
DOI: 10.2196/20979
Abstract: Neuropathic pain is a debilitating secondary condition for many in iduals with spinal cord injury. Spinal cord injury neuropathic pain often is poorly responsive to existing pharmacological and nonpharmacological treatments. A growing body of evidence supports the potential for brain-computer interface systems to reduce spinal cord injury neuropathic pain via electroencephalographic neurofeedback. However, further studies are needed to provide more definitive evidence regarding the effectiveness of this intervention. The primary objective of this study is to evaluate the effectiveness of a multiday course of a brain-computer interface neuromodulative intervention in a gaming environment to provide pain relief for in iduals with neuropathic pain following spinal cord injury. We have developed a novel brain-computer interface-based neuromodulative intervention for spinal cord injury neuropathic pain. Our brain-computer interface neuromodulative treatment includes an interactive gaming interface, and a neuromodulation protocol targeted to suppress theta (4-8 Hz) and high beta (20-30 Hz) frequency powers, and enhance alpha (9-12 Hz) power. We will use a single-case experimental design with multiple baselines to examine the effectiveness of our self-developed brain-computer interface neuromodulative intervention for the treatment of spinal cord injury neuropathic pain. We will recruit 3 participants with spinal cord injury neuropathic pain. Each participant will be randomly allocated to a different baseline phase (ie, 7, 10, or 14 days), which will then be followed by 20 sessions of a 30-minute brain-computer interface neuromodulative intervention over a 4-week period. The visual analog scale assessing average pain intensity will serve as the primary outcome measure. We will also assess pain interference as a secondary outcome domain. Generalization measures will assess quality of life, sleep quality, and anxiety and depressive symptoms, as well as resting-state electroencephalography and thalamic γ-aminobutyric acid concentration. This study was approved by the Human Research Committees of the University of New South Wales in July 2019 and the University of Technology Sydney in January 2020. We plan to begin the trial in October 2020 and expect to publish the results by the end of 2021. This clinical trial using single-case experimental design methodology has been designed to evaluate the effectiveness of a novel brain-computer interface neuromodulative treatment for people with neuropathic pain after spinal cord injury. Single-case experimental designs are considered a viable alternative approach to randomized clinical trials to identify evidence-based practices in the field of technology-based health interventions when recruitment of large s les is not feasible. Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12620000556943 bit.ly/2RY1jRx PRR1-10.2196/20979
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2004
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: IEEE
Date: 06-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-1999
DOI: 10.1109/72.774232
Abstract: A recurrent self-organizing neural fuzzy inference network (RSONFIN) is proposed in this paper. The RSONFIN is inherently a recurrent multilayered connectionist network for realizing the basic elements and functions of dynamic fuzzy inference, and may be considered to be constructed from a series of dynamic fuzzy rules. The temporal relations embedded in the network are built by adding some feedback connections representing the memory elements to a feedforward neural fuzzy network. Each weight as well as node in the RSONFIN has its own meaning and represents a special element in a fuzzy rule. There are no hidden nodes (i.e., no membership functions and fuzzy rules) initially in the RSONFIN. They are created on-line via concurrent structure identification (the construction of dynamic fuzzy if-then rules) and parameter identification (the tuning of the free parameters of membership functions). The structure learning together with the parameter learning forms a fast learning algorithm for building a small, yet powerful, dynamic neural fuzzy network. Two major characteristics of the RSONFIN can thus be seen: 1) the recurrent property of the RSONFIN makes it suitable for dealing with temporal problems and 2) no predetermination, like the number of hidden nodes, must be given, since the RSONFIN can find its optimal structure and parameters automatically and quickly. Moreover, to reduce the number of fuzzy rules generated, a flexible input partition method, the aligned clustering-based algorithm, is proposed. Various simulations on temporal problems are done and performance comparisons with some existing recurrent networks are also made. Efficiency of the RSONFIN is verified from these results.
Publisher: IEEE
Date: 2005
Publisher: Springer Science and Business Media LLC
Date: 08-06-2010
DOI: 10.1155/2010/945130
Publisher: MDPI AG
Date: 25-10-2019
DOI: 10.3390/MI10110720
Abstract: A brain–computer interface (BCI) is a type of interface/communication system that can help users interact with their environments. Electroencephalography (EEG) has become the most common application of BCIs and provides a way for disabled in iduals to communicate. While wet sensors are the most commonly used sensors for traditional EEG measurements, they require considerable preparation time, including the time needed to prepare the skin and to use the conductive gel. Additionally, the conductive gel dries over time, leading to degraded performance. Furthermore, requiring patients to wear wet sensors to record EEG signals is considered highly inconvenient. Here, we report a wireless 8-channel digital active-circuit EEG signal acquisition system that uses dry sensors. Active-circuit systems for EEG measurement allow people to engage in daily life while using these systems, and the advantages of these systems can be further improved by utilizing dry sensors. Moreover, the use of dry sensors can help both disabled and healthy people enjoy the convenience of BCIs in daily life. To verify the reliability of the proposed system, we designed three experiments in which we evaluated eye blinking and teeth gritting, measured alpha waves, and recorded event-related potentials (ERPs) to compare our developed system with a standard Neuroscan EEG system.
Publisher: IEEE
Date: 08-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2007
Publisher: IEEE
Date: 07-2018
Publisher: IEEE
Date: 07-2011
Publisher: IEEE
Date: 08-2015
Publisher: Springer Science and Business Media LLC
Date: 22-06-2021
DOI: 10.1038/S41598-021-92246-4
Abstract: Spatial navigation is a complex cognitive process based on multiple senses that are integrated and processed by a wide network of brain areas. Previous studies have revealed the retrosplenial complex (RSC) to be modulated in a task-related manner during navigation. However, these studies restricted participants’ movement to stationary setups, which might have impacted heading computations due to the absence of vestibular and proprioceptive inputs. Here, we present evidence of human RSC theta oscillation (4–8 Hz) in an active spatial navigation task where participants actively ambulated from one location to several other points while the position of a landmark and the starting location were updated. The results revealed theta power in the RSC to be pronounced during heading changes but not during translational movements, indicating that physical rotations induce human RSC theta activity. This finding provides a potential evidence of head-direction computation in RSC in healthy humans during active spatial navigation.
Publisher: arXiv
Date: 2021
Publisher: MDPI AG
Date: 08-11-2022
DOI: 10.3390/TECHNOLOGIES10060115
Abstract: The modelling of trust values on agents is broadly considered fundamental for decision-making in human-autonomous teaming (HAT) systems. Compared to the evaluation of trust values for robotic agents, estimating human trust is more challenging due to trust miscalibration issues, including undertrust and overtrust problems. From a subjective perception, human trust could be altered along with dynamic human cognitive states, which makes trust values hard to calibrate properly. Thus, in an attempt to capture the dynamics of human trust, the present study evaluated the dynamic nature of trust for human agents through real-time multievidence measures, including human states of attention, stress and perception abilities. The proposed multievidence human trust model applied an adaptive fusion method based on fuzzy reinforcement learning to fuse multievidence from eye trackers, heart rate monitors and human awareness. In addition, fuzzy reinforcement learning was applied to generate rewards via a fuzzy logic inference process that has tolerance for uncertainty in human physiological signals. The results of robot simulation suggest that the proposed trust model can generate reliable human trust values based on real-time cognitive states in the process of ongoing tasks. Moreover, the human-autonomous team with the proposed trust model improved the system efficiency by over 50% compared to the team with only autonomous agents. These results may demonstrate that the proposed model could provide insight into the real-time adaptation of HAT systems based on human states and, thus, might help develop new ways to enhance future HAT systems better.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2021
Publisher: IEEE
Date: 2001
Publisher: IEEE
Date: 06-2015
Publisher: IEEE
Date: 06-2009
Publisher: Elsevier BV
Date: 2018
DOI: 10.1016/J.BRAINRES.2017.11.016
Abstract: Studies on spatial navigation demonstrate a significant role of the retrosplenial complex (RSC) in the transformation of egocentric and allocentric information into complementary spatial reference frames (SRFs). The tight anatomical connections of the RSC with a wide range of other cortical regions processing spatial information support its vital role within the human navigation network. To better understand how different areas of the navigational network interact, we investigated the dynamic causal interactions of brain regions involved in solving a virtual navigation task. EEG signals were decomposed by independent component analysis (ICA) and subsequently examined for information flow between clusters of independent components (ICs) using direct short-time directed transfer function (sdDTF). The results revealed information flow between the anterior cingulate cortex and the left prefrontal cortex in the theta (4-7 Hz) frequency band and between the prefrontal, motor, parietal, and occipital cortices as well as the RSC in the alpha (8-13 Hz) frequency band. When participants prefered to use distinct reference frames (egocentric vs. allocentric) during navigation was considered, a dominant occipito-parieto-RSC network was identified in allocentric navigators. These results are in line with the assumption that the RSC, parietal, and occipital cortices are involved in transforming egocentric visual-spatial information into an allocentric reference frame. Moreover, the RSC demonstrated the strongest causal flow during changes in orientation, suggesting that this structure directly provides information on heading changes in humans.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2023
Publisher: IEEE
Date: 12-2013
Publisher: Frontiers Media SA
Date: 13-10-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2023
Publisher: IOP Publishing
Date: 24-09-2013
DOI: 10.1088/1741-2560/10/5/056024
Abstract: This study explores the neurophysiological changes, measured using an electroencephalogram (EEG), in response to an arousing warning signal delivered to drowsy drivers, and predicts the efficacy of the feedback based on changes in the EEG. Eleven healthy subjects participated in sustained-attention driving experiments. The driving task required participants to maintain their cruising position and compensate for randomly induced lane deviations using the steering wheel, while their EEG and driving performance were continuously monitored. The arousing warning signal was delivered to participants who experienced momentary behavioral lapses, failing to respond rapidly to lane-departure events (specifically the reaction time exceeded three times the alert reaction time). The results of our previous studies revealed that arousing feedback immediately reversed deteriorating driving performance, which was accompanied by concurrent EEG theta- and alpha-power suppression in the bilateral occipital areas. This study further proposes a feedback efficacy assessment system to accurately estimate the efficacy of arousing warning signals delivered to drowsy participants by monitoring the changes in their EEG power spectra immediately thereafter. The classification accuracy was up 77.8% for determining the need for triggering additional warning signals. The findings of this study, in conjunction with previous studies on EEG correlates of behavioral lapses, might lead to a practical closed-loop system to predict, monitor and rectify behavioral lapses of human operators in attention-critical settings.
Publisher: IEEE
Date: 12-2020
Publisher: Springer Science and Business Media LLC
Date: 27-08-2019
DOI: 10.1038/S41524-019-0224-X
Abstract: Topologically ordered materials may serve as a platform for new quantum technologies, such as fault-tolerant quantum computers. To fulfil this promise, efficient and general methods are needed to discover and classify new topological phases of matter. We demonstrate that deep neural networks augmented with external memory can use the density profiles formed in quantum walks to efficiently identify properties of a topological phase as well as phase transitions. On a trial topological ordered model, our method’s accuracy of topological phase identification reaches 97.4%, and is shown to be robust to noise on the data. Furthermore, we demonstrate that our trained DNN is able to identify topological phases of a perturbed model, and predict the corresponding shift of topological phase transitions without learning any information about the perturbations in advance. These results demonstrate that our approach is generally applicable and may be used to identify a variety of quantum topological materials.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2015
Publisher: IEEE
Date: 2001
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2002
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 1999
DOI: 10.1109/5326.777078
Publisher: IEEE
Date: 07-2020
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: IEEE
Date: 2005
Publisher: Springer Netherlands
Date: 1995
Publisher: IEEE
Date: 03-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2005
Abstract: This paper proposes a novel neural-network-based adaptive hybrid-reflectance three-dimensional (3-D) surface reconstruction model. The neural network automatically combines the diffuse and specular components into a hybrid model. The proposed model considers the characteristics of each point and the variant albedo to prevent the reconstructed surface from being distorted. The neural network inputs are the pixel values of the two-dimensional images to be reconstructed. The normal vectors of the surface can then be obtained from the output of the neural network after supervised learning, where the illuminant direction does not have to be known in advance. Finally, the obtained normal vectors are applied to enforce integrability when reconstructing 3-D objects. Facial images and images of other general objects were used to test the proposed approach. The experimental results demonstrate that the proposed neural-network-based adaptive hybrid-reflectance model can be successfully applied to objects generally, and perform 3-D surface reconstruction better than some existing approaches.
Publisher: Elsevier BV
Date: 04-1995
Publisher: SAGE Publications
Date: 29-09-2017
Abstract: Entropy-based approaches to understanding the temporal dynamics of complexity have revealed novel insights into various brain activities. Herein, electroencephalogram complexity before migraine attacks was examined using an inherent fuzzy entropy approach, allowing the development of an electroencephalogram-based classification model to recognize the difference between interictal and preictal phases. Forty patients with migraine without aura and 40 age-matched normal control subjects were recruited, and the resting-state electroencephalogram signals of their prefrontal and occipital areas were prospectively collected. The migraine phases were defined based on the headache diary, and the preictal phase was defined as within 72 hours before a migraine attack. The electroencephalogram complexity of patients in the preictal phase, which resembled that of normal control subjects, was significantly higher than that of patients in the interictal phase in the prefrontal area (FDR-adjusted p 0.05) but not in the occipital area. The measurement of test-retest reliability (n = 8) using the intra-class correlation coefficient was good with r1 = 0.73 ( p = 0.01). Furthermore, the classification model, support vector machine, showed the highest accuracy (76 ± 4%) for classifying interictal and preictal phases using the prefrontal electroencephalogram complexity. Entropy-based analytical methods identified enhancement or “normalization” of frontal electroencephalogram complexity during the preictal phase compared with the interictal phase. This classification model, using this complexity feature, may have the potential to provide a preictal alert to migraine without aura patients.
Publisher: Frontiers Media SA
Date: 10-02-2021
DOI: 10.3389/FNINS.2021.621365
Abstract: Many studies have reported that exercise can influence cognitive performance. But advancing our understanding of the interrelations between psychology and physiology in sports neuroscience requires the study of real-time brain dynamics during exercise in the field. Electroencephalography (EEG) is one of the most powerful brain imaging technologies. However, the limited portability and long preparation time of traditional wet-sensor systems largely limits their use to laboratory settings. Wireless dry-sensor systems are emerging with much greater potential for practical application in sports. Hence, in this paper, we use the BR8 wireless dry-sensor EEG system to measure P300 brain dynamics while cycling at various intensities. The preparation time was mostly less than 2 min as BR8 system’s dry sensors were able to attain the required skin-sensor interface impedance, enabling its operation without any skin preparation or application of conductive gel. Ten participants performed four sessions of a 3 min rapid serial visual presentation (RSVP) task while resting and while cycling. These four sessions were pre-CE (RSVP only), low-CE (RSVP in 40–50% of max heart rate), vigorous-CE (RSVP in 71–85% of max heart rate) and post-CE (RSVP only). The recorded brain signals demonstrate that the P300 litudes, observed at the Pz channel, for the target and non-target responses were significantly different in all four sessions. The results also show decreased reaction times to the visual attention task during vigorous exercise, enriching our understanding of the ways in which exercise can enhance cognitive performance. Even though only a single channel was evaluated in this study, the quality and reliability of the measurement using these dry sensor-based EEG systems is clearly demonstrated by our results. Further, the smooth implementation of the experiment with a dry system and the success of the data analysis demonstrate that wireless dry EEG devices can open avenues for real-time measurement of cognitive functions in athletes outside the laboratory.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: SPIE
Date: 09-02-2012
DOI: 10.1117/12.908131
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2017
Publisher: IEEE
Date: 2004
Publisher: IEEE
Date: 05-2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2021
Publisher: IEEE
Date: 07-2016
Publisher: Springer Science and Business Media LLC
Date: 13-07-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2019
Publisher: Elsevier BV
Date: 12-2017
Publisher: Elsevier BV
Date: 02-2010
DOI: 10.1016/J.NEUROIMAGE.2009.10.005
Abstract: This study investigates motion-sickness-related brain responses using a VR-based driving simulator on a motion platform with six degrees of freedom, which provides both visual and vestibular stimulations to induce motion sickness in a manner that is close to that in daily life. Subjects' brain dynamics associated with motion sickness were measured using a 32-channel EEG system. Their degree of motion sickness was simultaneously and continuously reported using an onsite joystick, providing non-stop behavioral references to the recorded EEG changes. The acquired EEG signals were parsed by independent component analysis (ICA) into maximally independent processes. The decomposition enables the brain dynamics that are induced by the motion of the platform and motion sickness to be disassociated. Five MS-related brain processes with equivalent dipoles located in the left motor, the parietal, the right motor, the occipital and the occipital midline areas were consistently identified across all subjects. The parietal and motor components exhibited significant alpha power suppression in response to vestibular stimuli, while the occipital components exhibited MS-related power augmentation in mainly theta and delta bands the occipital midline components exhibited a broadband power increase. Further, time series cross-correlation analysis was employed to evaluate relationships between the spectral changes associated with different brain processes and the degree of motion sickness. According to our results, it is suggested both visual and vestibular stimulations should be used to induce motion sickness in brain dynamic studies.
Publisher: The Scientific and Technological Research Council of Turkey (TUBITAK-ULAKBIM) - DIGITAL COMMONS JOURNALS
Date: 18-09-2019
DOI: 10.3906/ELK-1805-92
Publisher: SAGE Publications
Date: 08-2011
DOI: 10.2466/05.06.25.PMS.113.4.339-352
Abstract: Most research based on Fitts' law define a log-linear relationship between temporal and spatial accuracy in goal-directed aiming tasks using stationary targets. Whether this relationship holds or not when the targets have varying velocities, and how the behavioral strategies and physical activities may change accordingly are of interest. The aim of this study was to investigate the relationship between temporal and spatial accuracy in goal-directed aiming tasks with moving targets. Participants were asked to aim at two target widths using a joystick. Results demonstrated that in a goal-directed aiming task there was a negative effect on performance when target velocity was increased or target width was decreased. Participants moved faster and then made more systematic errors in a high-velocity target condition. Results may be applicable to the complex perceptual-motor behavior of people who perform tasks using computers.
Publisher: IEEE
Date: 1994
Publisher: IEEE
Date: 2005
Publisher: American Scientific Publishers
Date: 10-2013
Publisher: IEEE
Date: 2000
Publisher: Elsevier BV
Date: 2017
Publisher: Bentham Science Publishers Ltd.
Date: 28-01-2019
DOI: 10.2174/1567205017666200103112443
Abstract: Alzheimer’s disease, the most common cause of dementia among the elderly, is a progressive and irreversible neurodegenerative disease. Exposure to air pollutants is known to have adverse effects on human health, however, little is known about hydrocarbons in the air that can trigger a dementia event. We aimed to investigate whether long-term exposure to airborne hydrocarbons increases the risk of developing dementia. The present cohort study included 178,085 people aged 50 years and older in Taiwan. Cox proportional hazards regression analysis was used to fit the multiple pollutant models for two targeted pollutants, including total hydrocarbons and non-methane hydrocarbons, and estimated the risk of dementia. Before controlling for multiple pollutants, hazard ratios with 95% confidence intervals for the overall population were 7.63 (7.28-7.99, p .001) at a 0.51-ppm increases in total hydrocarbons, and 2.94 (2.82-3.05, p .001) at a 0.32-ppm increases in non-methane hydrocarbons. The highest adjusted hazard ratios for different multiple-pollutant models of each targeted pollutant were statistically significant (p .001) for all patients: 11.52 (10.86-12.24) for total hydrocarbons and 9.73 (9.18-10.32) for non-methane hydrocarbons. Our findings suggest that total hydrocarbons and non-methane hydrocarbons may be contributing to dementia development.
Publisher: MDPI AG
Date: 30-05-2011
DOI: 10.3390/S110605819
Publisher: Springer Science and Business Media LLC
Date: 2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2010
Publisher: IEEE
Date: 07-2014
Publisher: IEEE
Date: 05-1970
Publisher: Frontiers Media SA
Date: 04-04-2022
DOI: 10.3389/FNINS.2022.792318
Abstract: Brain-computer interfaces (BCI) relying on electroencephalography (EEG) based neuroimaging mode has shown prospects for real-world usage due to its portability and optional selectivity of fewer channels for compactness. However, noise and artifacts often limit the capacity of BCI systems especially for event-related potentials such as P300 and error-related negativity (ERN), whose biomarkers are present in short time segments at the time-series level. Contrary to EEG, invasive recording is less prone to noise but requires a tedious surgical procedure. But EEG signal is the result of aggregation of neuronal spiking information underneath the scalp surface and transforming the relevant BCI task's EEG signal to spike representation could potentially help improve the BCI performance. In this study, we designed an approach using a spiking neural network (SNN) which is trained using surrogate-gradient descent to generate task-related multi-channel EEG template signals of all classes. The trained model is in turn leveraged to obtain the latent spike representation for each EEG s le. Comparing the classification performance of EEG signal and its spike-representation, the proposed approach enhanced the performance of ERN dataset from 79.22 to 82.27% with naive bayes and for P300 dataset, the accuracy was improved from 67.73 to 69.87% using xGboost. In addition, principal component analysis and correlation metrics were evaluated on both EEG signals and their spike-representation to identify the reason for such improvement.
Publisher: IEEE
Date: 1992
Publisher: SPIE
Date: 04-03-2013
DOI: 10.1117/12.2003543
Publisher: Springer Science and Business Media LLC
Date: 22-08-2008
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: arXiv
Date: 2018
Publisher: IEEE
Date: 1995
Publisher: IEEE
Date: 1995
Publisher: arXiv
Date: 2018
Publisher: IEEE
Date: 07-2016
Publisher: MDPI AG
Date: 31-08-2020
DOI: 10.3390/A13090215
Abstract: This paper proposes a novel approach for selecting a subset of features in semi-supervised datasets where only some of the patterns are labeled. The whole process is completed in two phases. In the first phase, i.e., Phase-I, the whole dataset is ided into two parts: The first part, which contains labeled patterns, and the second part, which contains unlabeled patterns. In the first part, a small number of features are identified using well-known maximum relevance (from first part) and minimum redundancy (whole dataset) based feature selection approaches using the correlation coefficient. The subset of features from the identified set of features, which produces a high classification accuracy using any supervised classifier from labeled patterns, is selected for later processing. In the second phase, i.e., Phase-II, the patterns belonging to the first and second part are clustered separately into the available number of classes of the dataset. In the clusters of the first part, take the majority of patterns belonging to a cluster as the class for that cluster, which is given already. Form the pairs of cluster centroids made in the first and second part. The centroid of the second part nearest to a centroid of the first part will be paired. As the class of the first centroid is known, the same class can be assigned to the centroid of the cluster of the second part, which is unknown. The actual class of the patterns if known for the second part of the dataset can be used to test the classification accuracy of patterns in the second part. The proposed two-phase approach performs well in terms of classification accuracy and number of features selected on the given benchmarked datasets.
Publisher: Springer Science and Business Media LLC
Date: 13-09-2018
Publisher: World Scientific Pub Co Pte Lt
Date: 09-1995
DOI: 10.1142/S0129065795000214
Abstract: This paper addresses the structure and an associated on-line learning algorithm of a feedforward multilayer connectionist network for realizing the basic elements and functions of a traditional fuzzy logic controller. The proposed Fuzzy Adaptive Learning Control Network (FALCON) can be contrasted with the traditional fuzzy logic control systems in their network structure and learning ability. An on-line structure arameter learning algorithm, called FALCON-ART, is proposed for constructing the FALCON dynamically. The FALCON-ART can partition the input/output space in a flexible way based on the distribution of the training data. Hence it can avoid the problem of combinatorial growing of partitioned grids in some complex systems. It combines the backpropagation learning scheme for parameter learning and the fuzzy ART algorithm for structure learning. More notably, the FALCONART can on-line partition the input/output spaces, tune membership functions, and find proper fuzzy logic rules dynamically without any a priori knowledge or even any initial information on these. The proposed learning scheme has been successfully used to control two unstable nonlinear systems. They are the seesaw system and the inverted wedge system.
Publisher: IOP Publishing
Date: 10-04-2012
DOI: 10.1088/1741-2560/9/3/036001
Abstract: An implantable micromachined neural probe with multichannel electrode arrays for both neural signal recording and electrical stimulation was designed, simulated and experimentally validated for deep brain stimulation (DBS) applications. The developed probe has a rough three-dimensional microstructure on the electrode surface to maximize the electrode-tissue contact area. The flexible, polyimide-based microelectrode arrays were each composed of a long shaft (14.9 mm in length) and 16 electrodes (5 µm thick and with a diameter of 16 µm). The ability of these arrays to record and stimulate specific areas in a rat brain was evaluated. Moreover, we have developed a finite element model (FEM) applied to an electric field to evaluate the volume of tissue activated (VTA) by DBS as a function of the stimulation parameters. The signal-to-noise ratio ranged from 4.4 to 5 over a 50 day recording period, indicating that the laboratory-designed neural probe is reliable and may be used successfully for long-term recordings. The somatosensory evoked potential (SSEP) obtained by thalamic stimulations and in vivo electrode-electrolyte interface impedance measurements was stable for 50 days and demonstrated that the neural probe is feasible for long-term stimulation. A strongly linear (positive correlation) relationship was observed among the simulated VTA, the absolute value of the SSEP during the 200 ms post-stimulus period (ΣSSEP) and c-Fos expression, indicating that the simulated VTA has perfect sensitivity to predict the evoked responses (c-Fos expression). This laboratory-designed neural probe and its FEM simulation represent a simple, functionally effective technique for studying DBS and neural recordings in animal models.
Publisher: IEEE
Date: 11-2006
Publisher: IEEE
Date: 2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2009
Publisher: IEEE
Date: 10-2007
Publisher: IEEE
Date: 12-2015
Publisher: MDPI AG
Date: 27-12-2021
DOI: 10.3390/S22010151
Abstract: Stress is an inherent part of the normal human experience. Although, for the most part, this stress response is advantageous, chronic, heightened, or inappropriate stress responses can have deleterious effects on the human body. It has been suggested that in iduals who experience repeated or prolonged stress exhibit blunted biological stress responses when compared to the general population. Thus, when assessing whether a ubiquitous stress response exists, it is important to stratify based on resting levels in the absence of stress. Research has shown that stress that causes symptomatic responses requires early intervention in order to mitigate possible associated mental health decline and personal risks. Given this, real-time monitoring of stress may provide immediate biofeedback to the in idual and allow for early self-intervention. This study aimed to determine if the change in heart rate variability could predict, in two different cohorts, the quality of response to acute stress when exposed to an acute stressor and, in turn, contribute to the development of a physiological algorithm for stress which could be utilized in future smartwatch technologies. This study also aimed to assess whether baseline stress levels may affect the changes seen in heart rate variability at baseline and following stress tasks. A total of 30 student doctor participants and 30 participants from the general population were recruited for the study. The Trier Stress Test was utilized to induce stress, with resting and stress phase ECGs recorded, as well as inter-second heart rate (recorded using a FitBit). Although the present study failed to identify ubiquitous patterns of HRV and HR changes during stress, it did identify novel changes in these parameters between resting and stress states. This study has shown that the utilization of HRV as a measure of stress should be calculated with consideration of resting (baseline) anxiety and stress states in order to ensure an accurate measure of the effects of additive acute stress.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: IEEE
Date: 2005
DOI: 10.1109/BIBE.2005.26
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2000
DOI: 10.1109/36.841983
Publisher: Springer Science and Business Media LLC
Date: 04-05-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-1999
DOI: 10.1109/91.755398
Publisher: IEEE
Date: 2005
Publisher: IEEE
Date: 10-2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-05-2022
DOI: 10.36227/TECHRXIV.19691914.V1
Abstract: Clustering is a fundamental tool of scientific analysis, ubiquitous in disciplines from biology and chemistry to astronomy and pattern recognition. We propose a novel clustering algorithm based on the natural idea that a cluster and its nearest neighbor with higher mass should be merged into one cluster, unless they both have relatively large masses and the distance between them is also relatively large. The find of mass and distance peaks reveals the mergers that don’t conform to the rule and should be removed. The algorithm is parameter-free and harnesses this idea to recognize any cluster and find the proper number of clusters and noise autonomously. Experiments on numerous synthetic and real-world data sets show the enormous versatility of the proposed algorithm that remarkably outperforms the best compared algorithm. Additionally, we also compare it with latest state-of-the-art deep clustering algorithms on several challenging image data sets. The proposed algorithm without any deep representation achieves better or close performance than deep clustering algorithms on image clustering.
Publisher: IEEE
Date: 12-2015
Publisher: IEEE
Date: 11-2010
Publisher: IEEE
Date: 11-2010
Publisher: Cold Spring Harbor Laboratory
Date: 23-04-2022
DOI: 10.1101/2020.04.21.053512
Abstract: Detecting and correcting incorrect body movements is an essential part of everyday interaction with one’s environment. The human brain provides a monitoring system that constantly controls and adjusts our actions according to our surroundings. However, when our brain’s predictions about a planned action do not match the sensory inputs resulting from that action, cognitive conflict occurs. Much is known about cognitive conflict in 1D/2D environments however, less is known about the role of movement characteristics associated with cognitive conflict in 3D environment. Hence, we devised an object selection task in a virtual reality (VR) environment to test how the velocity of hand movements impacts human brain responses. From a series of analyses of EEG recordings synchronized with motion capture, we found that the velocity of the participants’ hand movements modulated the brain’s response to proprioceptive feedback during the task and induced a prediction error negativity (PEN). Additionally, the PEN originates in the anterior cingulate cortex and is itself modulated by the ballistic phase of the hand’s movement. These findings suggest that velocity is an essential component of integrating hand movements with visual and proprioceptive information during interactions with real and virtual objects.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2005
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2005
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2013
Publisher: Springer Science and Business Media LLC
Date: 23-12-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Elsevier BV
Date: 2013
DOI: 10.1016/J.IJPSYCHO.2012.11.003
Abstract: This study investigates the relationship between heart rate variability (HRV) and the level of motion sickness (MS) induced by simulated tunnel driving. The HRV indices, normalized low frequency (NLF, 0.04-0.15 Hz), normalized high frequency (NHF, 0.15-0.4 Hz), and LF/HF ratio were correlated with the subjectively and continuously rated MS levels of 20 participants. The experimental results showed that for 13 of the subjects, the MS levels positively correlated with the NLF and the LF/HF ratio and negatively correlated with the NHF. The remaining seven subjects had negative correlations between the MS levels and the NLF and the LF/HF ratio and a positive correlation between the MS levels and the NHF. To clarify this contradiction, this study also inspected the effects of subjects' self-adjustments on the correlations between the MS levels and the HRV indices and showed that the variations in the relationship might be attributed to the subjects' self-adjustments, which they used to relieve the discomfort of MS.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2004
DOI: 10.1109/TSMCB.2003.811518
Abstract: In this paper, a new technique for the Chinese text-to-speech (TTS) system is proposed. Our major effort focuses on the prosodic information generation. New methodologies for constructing fuzzy rules in a prosodic model simulating human's pronouncing rules are developed. The proposed Recurrent Fuzzy Neural Network (RFNN) is a multilayer recurrent neural network (RNN) which integrates a Self-cOnstructing Neural Fuzzy Inference Network (SONFIN) into a recurrent connectionist structure. The RFNN can be functionally ided into two parts. The first part adopts the SONFIN as a prosodic model to explore the relationship between high-level linguistic features and prosodic information based on fuzzy inference rules. As compared to conventional neural networks, the SONFIN can always construct itself with an economic network size in high learning speed. The second part employs a five-layer network to generate all prosodic parameters by directly using the prosodic fuzzy rules inferred from the first part as well as other important features of syllables. The TTS system combined with the proposed method can behave not only sandhi rules but also the other prosodic phenomena existing in the traditional TTS systems. Moreover, the proposed scheme can even find out some new rules about prosodic phrase structure. The performance of the proposed RFNN-based prosodic model is verified by imbedding it into a Chinese TTS system with a Chinese monosyllable database based on the time-domain pitch synchronous overlap add (TD-PSOLA) method. Our experimental results show that the proposed RFNN can generate proper prosodic parameters including pitch means, pitch shapes, maximum energy levels, syllable duration, and pause duration. Some synthetic sounds are online available for demonstration.
Publisher: Springer Science and Business Media LLC
Date: 26-04-2012
DOI: 10.1186/1687-6180-2012-92
Abstract: This study presents an appearance-based face recognition scheme called the nonparametric-weighted Fisherfaces (NW-Fisherfaces). Pixels in a facial image are considered as coordinates in a high-dimensional space and are transformed into a face subspace for analysis by using nonparametric-weighted feature extraction (NWFE). According to previous studies of hyperspectral image classification, NWFE is a powerful tool for extracting hyperspectral image features. The Fisherfaces method maximizes the ratio of between-class scatter to that of within-class scatter. In this study, the proposed NW-Fisherfaces weighted the between-class scatter to emphasize the boundary structure of the transformed face subspace and, therefore, enhances the separability for different persons' face. The proposed NW-Fisherfaces was compared with Orthogonal Laplacianfaces, Eigenfaces, Fisherfaces, direct linear discriminant analysis, and null space linear discriminant analysis methods for tests on five facial databases. Experimental results showed that the proposed approach outperforms other feature extraction methods for most databases.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2011
Publisher: IEEE
Date: 08-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: IEEE
Date: 11-2015
Start Date: 2021
End Date: 12-2024
Amount: $451,737.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2018
End Date: 12-2021
Amount: $366,663.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2022
End Date: 12-2024
Amount: $490,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2018
End Date: 12-2020
Amount: $354,592.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2015
End Date: 12-2018
Amount: $307,700.00
Funder: Australian Research Council
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