Toward Drowsiness Detection Using Non-hair-Bearing EEG-Based Brain-Computer Interfaces

Wei, CS; Wang, YT; Lin, CT; Jung, TP

Toward Drowsiness Detection Using Non-hair-Bearing EEG-Based Brain-Computer Interfaces

Wei, CS Wang, YT Lin, CT

Jung, TP

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2018, 26 (2), pp. 400 - 406
Issue Date:: 2018-02-01

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Field	Value	Language
dc.contributor.author	Wei, CS	en_US
dc.contributor.author	Wang, YT	en_US
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.contributor.author	Jung, TP https://orcid.org/0000-0002-8377-2166	en_US
dc.date.issued	2018-02-01	en_US
dc.identifier.citation	IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2018, 26 (2), pp. 400 - 406	en_US
dc.identifier.issn	1534-4320	en_US
dc.identifier.uri	http://hdl.handle.net/10453/131736
dc.description.abstract	© 2001-2011 IEEE. Drowsy driving is one of the major causes that lead to fatal accidents worldwide. For the past two decades, many studies have explored the feasibility and practicality of drowsiness detection using electroencephalogram (EEG)-based brain-computer interface (BCI) systems. However, on the pathway of transitioning laboratory-oriented BCI into real-world environments, one chief challenge is to obtain high-quality EEG with convenience and long-term wearing comfort. Recently, acquiring EEG from non-hair-bearing (NHB) scalp areas has been proposed as an alternative solution to avoid many of the technical limitations resulted from the interference of hair between electrodes and the skin. Furthermore, our pilot study has shown that informative drowsiness-related EEG features are accessible from the NHB areas. This study extends the previous work to quantitatively evaluate the performance of drowsiness detection using cross-session validation with widely studied machine-learning classifiers. The offline results showed no significant difference between the accuracy of drowsiness detection using the NHB EEG and the whole-scalp EEG across all subjects ( ${p} = \textsf {0.31}$ ). The findings of this study demonstrate the efficacy and practicality of the NHB EEG for drowsiness detection and could catalyze explorations and developments of many other real-world BCI applications.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP180100670
dc.relation	http://purl.org/au-research/grants/arc/DP180100656
dc.relation.ispartof	IEEE Transactions on Neural Systems and Rehabilitation Engineering	en_US
dc.relation.isbasedon	10.1109/TNSRE.2018.2790359	en_US
dc.subject.classification	Biomedical Engineering	en_US
dc.subject.mesh	Scalp	en_US
dc.subject.mesh	Hair	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Electroencephalography	en_US
dc.subject.mesh	Discriminant Analysis	en_US
dc.subject.mesh	Pilot Projects	en_US
dc.subject.mesh	Reproducibility of Results	en_US
dc.subject.mesh	Electrodes	en_US
dc.subject.mesh	Cognition	en_US
dc.subject.mesh	Wakefulness	en_US
dc.subject.mesh	Automobile Driving	en_US
dc.subject.mesh	Brain-Computer Interfaces	en_US
dc.subject.mesh	Support Vector Machine	en_US
dc.title	Toward Drowsiness Detection Using Non-hair-Bearing EEG-Based Brain-Computer Interfaces	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	26	en_US
utslib.for	0903 Biomedical Engineering	en_US
utslib.for	0906 Electrical and Electronic 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 Computer Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	26	en_US

Abstract:

© 2001-2011 IEEE. Drowsy driving is one of the major causes that lead to fatal accidents worldwide. For the past two decades, many studies have explored the feasibility and practicality of drowsiness detection using electroencephalogram (EEG)-based brain-computer interface (BCI) systems. However, on the pathway of transitioning laboratory-oriented BCI into real-world environments, one chief challenge is to obtain high-quality EEG with convenience and long-term wearing comfort. Recently, acquiring EEG from non-hair-bearing (NHB) scalp areas has been proposed as an alternative solution to avoid many of the technical limitations resulted from the interference of hair between electrodes and the skin. Furthermore, our pilot study has shown that informative drowsiness-related EEG features are accessible from the NHB areas. This study extends the previous work to quantitatively evaluate the performance of drowsiness detection using cross-session validation with widely studied machine-learning classifiers. The offline results showed no significant difference between the accuracy of drowsiness detection using the NHB EEG and the whole-scalp EEG across all subjects ( ${p} = \textsf {0.31}$ ). The findings of this study demonstrate the efficacy and practicality of the NHB EEG for drowsiness detection and could catalyze explorations and developments of many other real-world BCI applications.

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http://hdl.handle.net/10453/131736