ORCID Profile
0000-0002-4259-4121
Current Organisations
RIKEN Advanced Intelligence Project
,
Nicolaus Copernicus University
,
University of Tokyo
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Publisher: Springer International Publishing
Date: 2015
Publisher: IEEE
Date: 12-2015
Publisher: IEEE
Date: 08-2011
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: IEEE
Date: 12-2014
Publisher: Wiley
Date: 31-10-2007
Publisher: IEEE
Date: 08-2015
Publisher: Elsevier BV
Date: 2014
Publisher: IEEE
Date: 2006
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: IEEE
Date: 2000
Publisher: IEEE
Date: 09-10-2022
Publisher: Frontiers Media SA
Date: 29-05-2020
Publisher: Springer Singapore
Date: 2016
Publisher: IEEE
Date: 12-2014
Publisher: Elsevier BV
Date: 09-2020
Publisher: Springer International Publishing
Date: 2015
Publisher: Springer Science and Business Media LLC
Date: 10-12-2014
Publisher: IEEE
Date: 12-2014
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Springer Science and Business Media LLC
Date: 03-07-2007
Publisher: World Scientific Pub Co Pte Lt
Date: 02-2010
DOI: 10.1142/S0218126610006037
Abstract: Human brains exhibit a possibility to control directly the intelligent computing applications in form of brain computer/machine interfacing (BCI/BMI) technologies. Neurophysiological signals and especially electroencephalogram (EEG) are the forms of brain electrical activity which can be easily captured and utilized for BCI/BMI applications. Those signals are unfortunately usually very highly contaminated by external noise caused by the presence of different devices in the environment creating electromagnetic interference. In this paper, we first decompose each of the recorded channels, in multichannel EEG recording environment, into intrinsic mode functions (IMF) which are a result of empirical mode decomposition (EMD) extended to multichannel analysis. We present novel and interesting results on human mental and cognitive states estimation based on analysis of the above-mentioned stimuli-related IMF components. The IMF components are further clustered for their spectral similarity in order to identify only those carrying responses to present stimuli to the subjects. The resulting targets only reconstruction allows us to identify when and to which stimuli intelligent application user is tuning at a time.
Publisher: Elsevier BV
Date: 09-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2016
Publisher: IEEE
Date: 08-2007
Publisher: IEEE
Date: 10-2013
Publisher: Springer International Publishing
Date: 2015
Publisher: IEEE
Date: 12-2014
Publisher: IEEE
Date: 12-2014
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: IEEE
Date: 08-2009
Publisher: Springer Netherlands
Date: 2008
Publisher: Elsevier BV
Date: 09-2017
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: IEEE
Date: 12-2014
Publisher: Frontiers Media SA
Date: 2015
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11558637_15
Publisher: Springer Science and Business Media LLC
Date: 03-08-2010
Publisher: IEEE
Date: 08-2018
Publisher: IEEE
Date: 10-2012
Publisher: IEEE
Date: 12-2013
Publisher: IEEE
Date: 12-2013
Publisher: Polish Academy of Sciences Chancellery
Date: 12-2012
DOI: 10.2478/V10175-012-0055-0
Abstract: Established complexity measures typically operate at a single scale and thus fail to quantify inherent long-range correlations in real-world data, a key feature of complex systems. The recently introduced multiscale entropy (MSE) method has the ability to detect fractal correlations and has been used successfully to assess the complexity of univariate data. However, multivariate observations are common in many real-world scenarios and a simultaneous analysis of their structural complexity is a prerequisite for the understanding of the underlying signal-generating mechanism. For this purpose, based on the notion of multivariate s le entropy, the standard MSE method is extended to the multivariate case, whereby for rigor, the intrinsic multivariate scales of the input data are generated adaptively via the multivariate empirical mode decomposition (MEMD) algorithm. This allows us to gain better understanding of the complexity of the underlying multivariate real-world process, together with more degrees of freedom and physical interpretation in the analysis. Simulations on both synthetic and real-world biological multivariate data sets support the analysis.
Publisher: Springer International Publishing
Date: 2013
Publisher: IEEE
Date: 2002
Publisher: MDPI AG
Date: 04-10-2016
DOI: 10.20944/PREPRINTS201609.0126.V2
Abstract: The paper presents a study of two novel visual motion onset stimulus-based brain& ndash computer interfaces (vmoBCI). Two settings are compared with afferent and efferent to a computer screen center motion patterns. Online vmoBCI experiments are conducted in an oddball event& ndash related potential (ERP) paradigm allowing for & aha& ndash responses& decoding in EEG brainwaves. A subsequent stepwise linear discriminant analysis classification (swLDA) classification accuracy comparison is discussed based on two inter& ndash stimulus& ndash interval (ISI) settings of 700 and 150 ms in two online vmoBCI applications with six and eight command settings. A research hypothesis of classification accuracy non& ndash significant differences with various ISIs is confirmed based on the two settings of 700 ms and 150 ms, as well as with various numbers of ERP response averaging scenarios.The efferent in respect to display center visual motion patterns allowed for a faster interfacing and thus they are recommended as more suitable for the no& ndash eye& ndash movements requiring visual BCIs.
Publisher: IEEE
Date: 07-2020
Publisher: IEEE
Date: 10-2013
Publisher: MDPI AG
Date: 30-09-2016
DOI: 10.20944/PREPRINTS201609.0126.V1
Abstract: The paper presents a study of two novel visual motion onset stimulus-based brain& ndash computer interfaces (vmoBCI). Two settings are compared with afferent and efferent to a computer screen center motion patterns. Online vmoBCI experiments are conducted in an oddball event& ndash related potential (ERP) paradigm allowing for & aha& ndash responses& decoding in EEG brainwaves. A subsequent stepwise linear discriminant analysis classification (swLDA) classification accuracy comparison is discussed based on two inter& ndash stimulus& ndash interval (ISI) settings of 700 and 150 ms in two online vmoBCI applications with six and eight command settings. A research hypothesis of classification accuracy non& ndash significant differences with various ISIs is confirmed based on the two settings of 700 ms and 150 ms, as well as with various numbers of ERP response averaging scenarios.The efferent in respect to display center visual motion patterns allowed for a faster interfacing and thus they are recommended as more suitable for the no& ndash eye& ndash movements requiring visual BCIs.
Publisher: Frontiers Media SA
Date: 06-12-2016
Publisher: Springer Netherlands
Date: 2008
Publisher: IEEE
Date: 05-2019
Publisher: IEEE
Date: 12-2013
Publisher: IEEE
Date: 12-2015
Publisher: IEEE
Date: 12-2014
Publisher: IEEE
Date: 12-2015
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11679363_73
Publisher: IEEE
Date: 12-2015
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Elsevier BV
Date: 07-2007
Publisher: Springer International Publishing
Date: 2014
Publisher: Springer International Publishing
Date: 2015
Publisher: IEEE
Date: 2007
Publisher: IEEE
Date: 2010
Publisher: No publisher found
Date: 2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2008
DOI: 10.1109/MC.2008.431
Publisher: Springer Berlin Heidelberg
Date: 2003
Publisher: CSIRO Publishing
Date: 2015
DOI: 10.1071/SR13339
Abstract: There is an increasing interest in eucalypt reforestation for a range of purposes in Australia, including pulp-wood production, carbon mitigation and catchment water management. The impacts of this reforestation on soil water repellency have not been examined despite eucalypts often being associated with water repellency and water repellency having impacts on water movement across and within soils. To investigate the role of eucalypt reforestation on water repellency, and interactions with soil properties, we examined 31 sites across the south-west of Western Australia with paired plots differing only in present land use (pasture v. plantation). The incidence and severity of water repellency increased in the 5–8 years following reforestation with Eucalyptus globulus. Despite this difference in water repellency, there were no differences in soil characteristics, including soil organic carbon content or composition, between pasture and plantation soils, suggesting induction by small amounts of hydrophobic compounds from the trees. The incidence of soil water repellency was generally greater on sandy-surfaced ( % clay content) soils however, for these soils 72% of the pasture sites and 31% of the plantation were not water repellent, and this was independent of measured soil properties. Computer modelling revealed marked differences in the layering and packing of waxes on kaolinite and quartz surfaces, indicating the importance of interfacial interactions in the development of soil water repellency. The implications of increased water repellency for the management of eucalyptus plantations are considered.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2002
Publisher: Frontiers Media SA
Date: 2015
Publisher: IEEE
Date: 08-2015
Publisher: IEEE
Date: 10-2013
Publisher: IEEE
Date: 08-2015
Publisher: IEEE
Date: 12-2015
Publisher: Springer Science and Business Media LLC
Date: 18-05-2016
DOI: 10.1007/S10916-016-0520-7
Abstract: A novel multimodal and bio-inspired approach to biomedical signal processing and classification is presented in the paper. This approach allows for an automatic semantic labeling (interpretation) of sleep apnea events based the proposed data-driven biomedical signal processing and classification. The presented signal processing and classification methods have been already successfully applied to real-time unimodal brainwaves (EEG only) decoding in brain-computer interfaces developed by the author. In the current project the very encouraging results are obtained using multimodal biomedical (brainwaves and peripheral physiological) signals in a unified processing approach allowing for the automatic semantic data description. The results thus support a hypothesis of the data-driven and bio-inspired signal processing approach validity for medical data semantic interpretation based on the sleep apnea events machine-learning-related classification.
Publisher: Elsevier BV
Date: 11-2012
Publisher: IEEE
Date: 10-2013
Publisher: Frontiers Media SA
Date: 10-02-2023
Publisher: IEEE
Date: 07-2013
Publisher: IEEE
Date: 12-2014
Publisher: Springer International Publishing
Date: 2016
Publisher: Frontiers Media SA
Date: 16-06-2023
DOI: 10.3389/FNHUM.2023.1155194
Abstract: Modern neurotechnology research employing state-of-the-art machine learning algorithms within the so-called “AI for social good” domain contributes to improving the well-being of in iduals with a disability. Using digital health technologies, home-based self-diagnostics, or cognitive decline managing approaches with neuro-biomarker feedback may be helpful for older adults to remain independent and improve their wellbeing. We report research results on early-onset dementia neuro-biomarkers to scrutinize cognitive-behavioral intervention management and digital non-pharmacological therapies. We present an empirical task in the EEG-based passive brain-computer interface application framework to assess working memory decline for forecasting a mild cognitive impairment. The EEG responses are analyzed in a framework of a network neuroscience technique applied to EEG time series for evaluation and to confirm the initial hypothesis of possible ML application modeling mild cognitive impairment prediction. We report findings from a pilot study group in Poland for a cognitive decline prediction. We utilize two emotional working memory tasks by analyzing EEG responses to facial emotions reproduced in short videos. A reminiscent interior image oddball task is also employed to validate the proposed methodology further. The proposed three experimental tasks in the current pilot study showcase the critical utilization of artificial intelligence for early-onset dementia prognosis in older adults.
Publisher: Informa UK Limited
Date: 2021
DOI: 10.2147/RMHP.S278774
Publisher: IEEE
Date: 10-2013
Publisher: Springer Berlin Heidelberg
Date: 2004
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: Elsevier BV
Date: 04-2015
DOI: 10.1016/J.JNEUMETH.2014.04.010
Abstract: The paper presents a report on the recently developed BCI alternative for users suffering from impaired vision (lack of focus or eye-movements) or from the so-called "ear-blocking-syndrome" (limited hearing). We report on our recent studies of the extents to which vibrotactile stimuli delivered to the head of a user can serve as a platform for a brain computer interface (BCI) paradigm. In the proposed tactile and bone-conduction auditory BCI novel multiple head positions are used to evoke combined somatosensory and auditory (via the bone conduction effect) P300 brain responses, in order to define a multimodal tactile and bone-conduction auditory brain computer interface (tbcaBCI). In order to further remove EEG interferences and to improve P300 response classification synchrosqueezing transform (SST) is applied. SST outperforms the classical time-frequency analysis methods of the non-linear and non-stationary signals such as EEG. The proposed method is also computationally more effective comparing to the empirical mode decomposition. The SST filtering allows for online EEG preprocessing application which is essential in the case of BCI. Experimental results with healthy BCI-naive users performing online tbcaBCI, validate the paradigm, while the feasibility of the concept is illuminated through information transfer rate case studies. We present a comparison of the proposed SST-based preprocessing method, combined with a logistic regression (LR) classifier, together with classical preprocessing and LDA-based classification BCI techniques. The proposed tbcaBCI paradigm together with data-driven preprocessing methods are a step forward in robust BCI applications research.
Publisher: IEEE
Date: 04-2009
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: Elsevier BV
Date: 2009
Publisher: IEEE
Date: 07-2007
Publisher: IEEE
Date: 11-2013
Publisher: IEEE
Date: 11-2012
Publisher: IEEE
Date: 11-2012
Publisher: Springer Berlin Heidelberg
Date: 2007
Publisher: IEEE
Date: 08-2009
Publisher: Springer Science and Business Media LLC
Date: 06-08-2013
Publisher: Springer Science and Business Media LLC
Date: 24-11-2010
Publisher: MDPI AG
Date: 06-2017
DOI: 10.3390/RS9060545
Publisher: IEEE
Date: 2001
Publisher: Springer Netherlands
Date: 22-10-2010
Publisher: Springer Netherlands
Date: 22-10-2010
Publisher: Springer Netherlands
Date: 22-10-2010
Publisher: IEEE
Date: 12-2014
Publisher: Elsevier BV
Date: 2008
Publisher: IEEE
Date: 1999
Publisher: IEEE
Date: 11-07-2022
Publisher: Cold Spring Harbor Laboratory
Date: 19-05-2021
DOI: 10.1101/2021.05.18.21257366
Abstract: An increase in dementia cases is producing significant medical and economic pressure in many communities. This growing problem calls for the application of AI-based technologies to support early diagnostics, and for subsequent non-pharmacological cognitive interventions and mental well-being monitoring. We present a practical application of a machine learning (ML) model in the domain known as ‘AI for social good’. In particular, we focus on early dementia onset prediction from speech patterns in natural conversation situations. This paper explains our model and study results of conversational speech pattern-based prognostication of mild dementia onset indicated by predictive Mini-Mental State Exam (MMSE) scores. Experiments with elderly subjects are conducted in natural conversation situations, with four members in each study group. We analyze the resulting four-party conversation speech transcripts within a natural language processing (NLP) deep learning framework to obtain conversation embedding. With a fully connected deep learning model, we use the conversation topic changing distances for subsequent MMSE score prediction. This pilot study is conducted with Japanese elderly subjects within a healthy group. The best median MMSE prediction errors are at the level of 0.167, with a median coefficient of determination equal to 0.330 and a mean absolute error of 0.909. The results presented are easily reproducible for other languages by swapping the language model in the proposed deep-learning conversation embedding approach.
Publisher: IEEE
Date: 03-2012
Publisher: IEEE
Date: 03-2012
Publisher: The Royal Society
Date: 09-2021
Abstract: The role of whole-genome duplication (WGD) in facilitating shifts into novel biomes remains unknown. Focusing on two erse woody plant groups in New Zealand, Coprosma (Rubiaceae) and Veronica (Plantaginaceae), we investigate how biome occupancy varies with ploidy level, and test the hypothesis that WGD increases the rate of biome shifting. Ploidy levels and biome occupancy (forest, open and alpine) were determined for indigenous species in both clades. The distribution of low-ploidy ( Coprosma : 2 x , Veronica : 6 x ) versus high-ploidy ( Coprosma : 4–10 x , Veronica : 12–18 x ) species across biomes was tested statistically. Estimation of the phylogenetic history of biome occupancy and WGD was performed using time-calibrated phylogenies and the R package BioGeoBEARS. Trait-dependent dispersal models were implemented to determine support for an increased rate of biome shifting among high-ploidy lineages. We find support for a greater than random portion of high-ploidy species occupying multiple biomes. We also find strong support for high-ploidy lineages showing a three- to eightfold increase in the rate of biome shifts. These results suggest that WGD promotes ecological expansion into new biomes.
Publisher: Springer US
Date: 2008
Publisher: Springer Netherlands
Date: 2008
Location: Australia
No related grants have been discovered for Tomasz Rutkowski.