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MRMR based feature selection for the classification of stress using EEG

Mental stress is a social concern causing functional disability during work routines. The evaluation of stress using electroencephalogram signals is a topic of contemporary research. EEG provides several different features and the selection of appropriate features becomes a question. This study pres...

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Main Authors: Subhani, A.R., Mumtaz, W., Kamil, N., Saad, N.M., Nandagopal, N., Malik, A.S.
Format: Article
Published: IEEE Computer Society 2018
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045674174&doi=10.1109%2fICSensT.2017.8304499&partnerID=40&md5=6c481b14bd0905109e0ac5b3b4201eec
http://eprints.utp.edu.my/21755/
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spelling my.utp.eprints.217552018-11-16T08:32:58Z MRMR based feature selection for the classification of stress using EEG Subhani, A.R. Mumtaz, W. Kamil, N. Saad, N.M. Nandagopal, N. Malik, A.S. Mental stress is a social concern causing functional disability during work routines. The evaluation of stress using electroencephalogram signals is a topic of contemporary research. EEG provides several different features and the selection of appropriate features becomes a question. This study presents the utilization of feature selection using maximum relevance and minimum redundancy (MRMR) based on mutual information (MI) on the obtained features from electroencephalogram (EEG) signals during stress and control tasks. We moved forward in recording EEG during stress which was induced by taking up an eminent experimental model based on the Montreal Imaging Stress Task (MIST). The induced stress was endorsed by the performance during the task and the response of the subjects. The methodology consist of EEG feature extraction such as the absolute power and relative power, feature selection (MI) and classification using the support vector machine. The results of the proposed methodology showed a maximum accuracy of 93.75 and above 85 accuracy throughout the experiment. The performance is better than the existing studies in the literature. In conclusion, the MRMR criterion of feature selection using MI gives reliable and consistent results for the classification of stress. © 2017 IEEE. IEEE Computer Society 2018 Article PeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045674174&doi=10.1109%2fICSensT.2017.8304499&partnerID=40&md5=6c481b14bd0905109e0ac5b3b4201eec Subhani, A.R. and Mumtaz, W. and Kamil, N. and Saad, N.M. and Nandagopal, N. and Malik, A.S. (2018) MRMR based feature selection for the classification of stress using EEG. Proceedings of the International Conference on Sensing Technology, ICST, 2017-D . pp. 1-4. http://eprints.utp.edu.my/21755/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description Mental stress is a social concern causing functional disability during work routines. The evaluation of stress using electroencephalogram signals is a topic of contemporary research. EEG provides several different features and the selection of appropriate features becomes a question. This study presents the utilization of feature selection using maximum relevance and minimum redundancy (MRMR) based on mutual information (MI) on the obtained features from electroencephalogram (EEG) signals during stress and control tasks. We moved forward in recording EEG during stress which was induced by taking up an eminent experimental model based on the Montreal Imaging Stress Task (MIST). The induced stress was endorsed by the performance during the task and the response of the subjects. The methodology consist of EEG feature extraction such as the absolute power and relative power, feature selection (MI) and classification using the support vector machine. The results of the proposed methodology showed a maximum accuracy of 93.75 and above 85 accuracy throughout the experiment. The performance is better than the existing studies in the literature. In conclusion, the MRMR criterion of feature selection using MI gives reliable and consistent results for the classification of stress. © 2017 IEEE.
format Article
author Subhani, A.R.
Mumtaz, W.
Kamil, N.
Saad, N.M.
Nandagopal, N.
Malik, A.S.
spellingShingle Subhani, A.R.
Mumtaz, W.
Kamil, N.
Saad, N.M.
Nandagopal, N.
Malik, A.S.
MRMR based feature selection for the classification of stress using EEG
author_facet Subhani, A.R.
Mumtaz, W.
Kamil, N.
Saad, N.M.
Nandagopal, N.
Malik, A.S.
author_sort Subhani, A.R.
title MRMR based feature selection for the classification of stress using EEG
title_short MRMR based feature selection for the classification of stress using EEG
title_full MRMR based feature selection for the classification of stress using EEG
title_fullStr MRMR based feature selection for the classification of stress using EEG
title_full_unstemmed MRMR based feature selection for the classification of stress using EEG
title_sort mrmr based feature selection for the classification of stress using eeg
publisher IEEE Computer Society
publishDate 2018
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045674174&doi=10.1109%2fICSensT.2017.8304499&partnerID=40&md5=6c481b14bd0905109e0ac5b3b4201eec
http://eprints.utp.edu.my/21755/
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score 13.211869

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