Comparing features extractors in EEG-based cognitive fatigue detection of demanding computer tasks

Chai, R; Smith, MR; Nguyen, TN; Ling, SH; Coutts, AJ; Nguyen, HT

Comparing features extractors in EEG-based cognitive fatigue detection of demanding computer tasks

Chai, R

Smith, MR

Nguyen, TN

Ling, SH

Coutts, AJ

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. 7594 - 7597
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	Smith, MR https://orcid.org/0000-0002-8168-5405	en_US
dc.contributor.author	Nguyen, TN https://orcid.org/0000-0002-9958-9876	en_US
dc.contributor.author	Ling, SH https://orcid.org/0000-0003-0849-5098	en_US
dc.contributor.author	Coutts, AJ https://orcid.org/0000-0002-1782-7691	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. 7594 - 7597	en_US
dc.identifier.isbn	9781424492718	en_US
dc.identifier.issn	1557-170X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/37687
dc.identifier.uri	http://hdl.handle.net/10453/37686
dc.description.abstract	© 2015 IEEE. An electroencephalography (EEG)-based classification system could be used as a tool for detecting cognitive fatigue from demanding computer tasks. The most widely used feature extractor in EEG-based fatigue classification is power spectral density (PSD). This paper investigates PSD and three alternative feature extraction methods, in order to find the best feature extractor for the classification of cognitive fatigue during cognitively demanding tasks. These compared methods are power spectral entropy (PSE), wavelet, and autoregressive (AR). Bayesian neural network was selected as the classifier in this study. The results showed that the use of PSD and PSE methods provide an average accuracy of 60% for each computer task. This finding is slightly improved using the wavelet method which has an average accuracy of 61%. The AR method is the best feature extractor compared with the PSD, PSE and wavelet in this study with accuracy of 75.95% in AX-continuous performance test (AX-CPT), 75.23% in psychomotor vigilance test (PVT) and 76.02% in Stroop task (p-value < 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.2015.7320150	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Electroencephalography	en_US
dc.subject.mesh	Bayes Theorem	en_US
dc.subject.mesh	Mental Fatigue	en_US
dc.subject.mesh	Cognition	en_US
dc.subject.mesh	Task Performance and Analysis	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Neural Networks (Computer)	en_US
dc.subject.mesh	Computers	en_US
dc.subject.mesh	Signal Processing, Computer-Assisted	en_US
dc.subject.mesh	Adolescent	en_US
dc.subject.mesh	Adult	en_US
dc.subject.mesh	Young Adult	en_US
dc.subject.mesh	Neural Networks, Computer	en_US
dc.title	Comparing features extractors in EEG-based cognitive fatigue detection of demanding computer tasks	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 Health
pubs.organisational-group	/University of Technology Sydney/Strength - CHSP - Health Services and Practice
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	2015-November	en_US

Abstract:

© 2015 IEEE. An electroencephalography (EEG)-based classification system could be used as a tool for detecting cognitive fatigue from demanding computer tasks. The most widely used feature extractor in EEG-based fatigue classification is power spectral density (PSD). This paper investigates PSD and three alternative feature extraction methods, in order to find the best feature extractor for the classification of cognitive fatigue during cognitively demanding tasks. These compared methods are power spectral entropy (PSE), wavelet, and autoregressive (AR). Bayesian neural network was selected as the classifier in this study. The results showed that the use of PSD and PSE methods provide an average accuracy of 60% for each computer task. This finding is slightly improved using the wavelet method which has an average accuracy of 61%. The AR method is the best feature extractor compared with the PSD, PSE and wavelet in this study with accuracy of 75.95% in AX-continuous performance test (AX-CPT), 75.23% in psychomotor vigilance test (PVT) and 76.02% in Stroop task (p-value < 0.05).

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http://hdl.handle.net/10453/37686