Classification of driver fatigue in an electroencephalography-based countermeasure system with source separation module

Chai, R; Naik, GR; Tran, Y; Ling, SH; Craig, A; Nguyen, HT

Classification of driver fatigue in an electroencephalography-based countermeasure system with source separation module

Chai, R

Naik, GR

Tran, Y

Ling, SH

Craig, A

Nguyen, HT

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2015, 2015-November pp. 514 - 517
Issue Date:: 2015-11-04

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Field	Value	Language
dc.contributor.author	Chai, R https://orcid.org/0000-0002-1922-7024	en_US
dc.contributor.author	Naik, GR https://orcid.org/0000-0003-1790-9838	en_US
dc.contributor.author	Tran, Y https://orcid.org/0000-0002-1741-4205	en_US
dc.contributor.author	Ling, SH https://orcid.org/0000-0003-0849-5098	en_US
dc.contributor.author	Craig, A https://orcid.org/0000-0001-7647-7604	en_US
dc.contributor.author	Nguyen, HT https://orcid.org/0000-0003-3373-8178	en_US
dc.date.available	2015-06-01	en_US
dc.date.issued	2015-11-04	en_US
dc.identifier.citation	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2015, 2015-November pp. 514 - 517	en_US
dc.identifier.isbn	9781424492718	en_US
dc.identifier.issn	1557-170X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/37685
dc.description.abstract	© 2015 IEEE. An electroencephalography (EEG)-based counter measure device could be used for fatigue detection during driving. This paper explores the classification of fatigue and alert states using power spectral density (PSD) as a feature extractor and fuzzy swarm based-artificial neural network (ANN) as a classifier. An independent component analysis of entropy rate bound minimization (ICA-ERBM) is investigated as a novel source separation technique for fatigue classification using EEG analysis. A comparison of the classification accuracy of source separator versus no source separator is presented. Classification performance based on 43 participants without the inclusion of the source separator resulted in an overall sensitivity of 71.67%, a specificity of 75.63% and an accuracy of 73.65%. However, these results were improved after the inclusion of a source separator module, resulting in an overall sensitivity of 78.16%, a specificity of 79.60% and an accuracy of 78.88% (p < 0.05).	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP150102493
dc.relation.ispartof	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS	en_US
dc.relation.isbasedon	10.1109/EMBC.2015.7318412	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Fatigue	en_US
dc.subject.mesh	Electroencephalography	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Neural Networks (Computer)	en_US
dc.subject.mesh	Entropy	en_US
dc.subject.mesh	Signal Processing, Computer-Assisted	en_US
dc.subject.mesh	Neural Networks, Computer	en_US
dc.title	Classification of driver fatigue in an electroencephalography-based countermeasure system with source separation module	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2015-November	en_US
utslib.for	0903 Biomedical Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	2015-November	en_US

Abstract:

© 2015 IEEE. An electroencephalography (EEG)-based counter measure device could be used for fatigue detection during driving. This paper explores the classification of fatigue and alert states using power spectral density (PSD) as a feature extractor and fuzzy swarm based-artificial neural network (ANN) as a classifier. An independent component analysis of entropy rate bound minimization (ICA-ERBM) is investigated as a novel source separation technique for fatigue classification using EEG analysis. A comparison of the classification accuracy of source separator versus no source separator is presented. Classification performance based on 43 participants without the inclusion of the source separator resulted in an overall sensitivity of 71.67%, a specificity of 75.63% and an accuracy of 73.65%. However, these results were improved after the inclusion of a source separator module, resulting in an overall sensitivity of 78.16%, a specificity of 79.60% and an accuracy of 78.88% (p < 0.05).

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http://hdl.handle.net/10453/37685