R-principal subspace for driver cognitive state classification

Using EEG signals, a novel technique for driver cognitive state assessment is presented, analyzed and experimentally verified. The proposed technique depends on the singular value decomposition (SVD) in finding the distributed energy of the EEG data matrix A in the direction of the r-principal subsp...

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Main Authors: Almahasneh, H., Kamel, N., Walter, N., Malik, A.S.
Format: Conference or Workshop Item
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.26192 /
Published: Institute of Electrical and Electronics Engineers Inc. 2015
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84953315533&doi=10.1109%2fEMBC.2015.7319300&partnerID=40&md5=098d0593e80cc929afe804db1c5ba2b3
http://eprints.utp.edu.my/26192/
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id utp-eprints.26192
recordtype eprints
spelling utp-eprints.261922021-08-30T08:54:19Z R-principal subspace for driver cognitive state classification Almahasneh, H. Kamel, N. Walter, N. Malik, A.S. Using EEG signals, a novel technique for driver cognitive state assessment is presented, analyzed and experimentally verified. The proposed technique depends on the singular value decomposition (SVD) in finding the distributed energy of the EEG data matrix A in the direction of the r-principal subspace. This distribution is unique and sensitive to the changes in the cognitive state of the driver due to external stimuli, so it is used as a set of features for classification. The proposed technique is tested with 42 subjects using 128 EEG channels and the results show significant improvements in terms of accuracy, specificity, sensitivity, and false detection in comparison to other recently proposed techniques. © 2015 IEEE. Institute of Electrical and Electronics Engineers Inc. 2015 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-84953315533&doi=10.1109%2fEMBC.2015.7319300&partnerID=40&md5=098d0593e80cc929afe804db1c5ba2b3 Almahasneh, H. and Kamel, N. and Walter, N. and Malik, A.S. (2015) R-principal subspace for driver cognitive state classification. In: UNSPECIFIED. http://eprints.utp.edu.my/26192/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description Using EEG signals, a novel technique for driver cognitive state assessment is presented, analyzed and experimentally verified. The proposed technique depends on the singular value decomposition (SVD) in finding the distributed energy of the EEG data matrix A in the direction of the r-principal subspace. This distribution is unique and sensitive to the changes in the cognitive state of the driver due to external stimuli, so it is used as a set of features for classification. The proposed technique is tested with 42 subjects using 128 EEG channels and the results show significant improvements in terms of accuracy, specificity, sensitivity, and false detection in comparison to other recently proposed techniques. © 2015 IEEE.
format Conference or Workshop Item
author Almahasneh, H.
Kamel, N.
Walter, N.
Malik, A.S.
spellingShingle Almahasneh, H.
Kamel, N.
Walter, N.
Malik, A.S.
R-principal subspace for driver cognitive state classification
author_sort Almahasneh, H.
title R-principal subspace for driver cognitive state classification
title_short R-principal subspace for driver cognitive state classification
title_full R-principal subspace for driver cognitive state classification
title_fullStr R-principal subspace for driver cognitive state classification
title_full_unstemmed R-principal subspace for driver cognitive state classification
title_sort r-principal subspace for driver cognitive state classification
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2015
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-84953315533&doi=10.1109%2fEMBC.2015.7319300&partnerID=40&md5=098d0593e80cc929afe804db1c5ba2b3
http://eprints.utp.edu.my/26192/
_version_ 1741197097985638400
score 11.62408

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