ORCID Profile
0000-0003-2138-8334
Current Organisations
Kent Institute Australia Pty Ltd
,
Asia Pacific International College
,
Charles Sturt University
,
Torrens University Australia
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Publisher: National Taiwan University
Date: 27-06-2019
DOI: 10.4015/S1016237219500327
Abstract: Cognitive load and emotional states may impact the cognitive learning process. Detection of reliable emotions and cognitive load would benefit the design of the emotional intelligent mobility system for visually impaired peoples (VIPs). Application of learning process using electroencephalography (EEG) offers novel and promising approaches to measure cognitive load and emotional states. EEG is used to identify the physiological index that can lead to detecting cognitive load and emotions which can help to explore the knowledge of learning processes. Basically, EEG is a record of ongoing electrical signals to represent the human brain activity due to external and internal stimuli. Therefore, in this study EEG signals are captured from participants with nine different degrees of sight loss people. EEG signals are then used to measure various cognitive load and emotional states to evaluate cognitive learning process for the VIPs. To support the argument of cognitive learning process, the complexity of the tasks in terms of cognitive load and emotional states are quantified considering erse factors by extracting features from various well-established metrics such as permutation entropy, event related synchronization/desynchronization, arousal, and valence when VIPs are navigating unfamiliar indoor environments. A classification accuracy of door is 86.67% which is achieved by the proposed model. It has almost 10% of improvement compared to another state-of the-art method who have used same dataset. Moreover, we have achieved 10% and 1% more accuracy in the corridor and open space conditions compared to the existing method. Experimental results also demonstrated that learning process is significantly improved considering wide range of obstacles when they are navigating indoor environments.
Publisher: IEEE
Date: 12-2012
Publisher: Technische Universität Berlin
Date: 2018
Publisher: Institution of Engineering and Technology (IET)
Date: 08-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2017
Publisher: Elsevier BV
Date: 12-2014
Publisher: Elsevier BV
Date: 2021
Publisher: Elsevier BV
Date: 04-2021
Publisher: National Taiwan University
Date: 28-05-2015
DOI: 10.4015/S1016237215500271
Abstract: Electroencephalogram (EEG) is a record of ongoing electrical signal to represent the human brain activity. It has great potential for the diagnosis to treatment of mental disorder and brain diseases such as epileptic seizure. Features extraction and classification is a crucial task to detect the stage of ictal (i.e. seizure period) and interictal (i.e. period between seizures) EEG signals for the treatment and precaution of the patient. However, existing seizure and non-seizure feature extraction techniques are not good enough for the classification of ictal and interictal EEG signals considering their non-abrupt phenomena and inconsistency in different brain locations. In this paper, we present new approaches for feature extraction using high-frequency components from discrete cosine transformation (DCT) and intrinsic mode function (IMF) extracted from empirical mode decomposition (EMD). These features are then used as an input to least square-support vector machine (LV-SVM) to classify ictal and interictal EEG signals. Experimental results show that the proposed methods outperform the existing state-of-the-art method for better classification in terms of sensitivity, specificity, and accuracy with greater consistence of ictal and interictal period of epilepsy for benchmark dataset from different brain locations.
Publisher: IEEE
Date: 2020
Publisher: IEEE
Date: 10-2019
Publisher: IGI Global
Date: 2016
DOI: 10.4018/978-1-4666-8811-7.CH015
Abstract: Epilepsy is one of the common neurological disorders characterized by a sudden and recurrent malfunction of the brain that is termed “seizure”, affecting around 65 million in iduals worldwide. Epileptic seizure may lead to many injuries such as fractures, submersion, burns, motor vehicle accidents and even death. It is highly possible to prevent these unwanted situations if we can predict/detect electrical changes in brain that occur prior to onset of actual seizure. When building a prediction model, the goal should be to make a model that accurately classifies preictal period (prior to a seizure onset) from interictal (period between seizures when non-seizure syndrome is observed) period. On the hand, for the detection we need to make a model that can classify ictal (actual seizure period) from non-ictal/interictal period. This chapter describes the seizure detection and prediction techniques with its background, features, recent developments, and future trends.
Publisher: IEEE
Date: 12-2020
Publisher: IEEE
Date: 2020
Publisher: Elsevier BV
Date: 06-2022
Publisher: EJournal Publishing
Date: 2015
Publisher: IEEE
Date: 08-2015
Publisher: IEEE
Date: 09-11-2020
Publisher: Springer International Publishing
Date: 2021
Publisher: Hindawi Limited
Date: 2018
DOI: 10.1155/2018/8971206
Abstract: Reliable detection of cognitive load would benefit the design of intelligent assistive navigation aids for the visually impaired (VIP). Ten participants with various degrees of sight loss navigated in unfamiliar indoor and outdoor environments, while their electroencephalogram (EEG) and electrodermal activity (EDA) signals were being recorded. In this study, the cognitive load of the tasks was assessed in real time based on a modification of the well-established event-related (de)synchronization (ERD/ERS) index. We present an in-depth analysis of the environments that mostly challenge people from certain categories of sight loss and we present an automatic classification of the perceived difficulty in each time instance, inferred from their biosignals. Given the limited size of our s le, our findings suggest that there are significant differences across the environments for the various categories of sight loss. Moreover, we exploit cross-modal relations predicting the cognitive load in real time inferring on features extracted from the EDA. Such possibility paves the way for the design on less invasive, wearable assistive devices that take into consideration the well-being of the VIP.
Publisher: Springer Nature Switzerland
Date: 2023
Publisher: IEEE
Date: 2020
Publisher: IEEE
Date: 03-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2016
Location: Italy
No related grants have been discovered for Mohammad Zavid Parvez.