EEG-based drowsiness estimation for safety driving using independent component analysis

Lin, CT; Wu, RC; Liang, SF; Chao, WH; Chen, YJ; Jung, TP

EEG-based drowsiness estimation for safety driving using independent component analysis

Lin, CT

Wu, RC Liang, SF Chao, WH Chen, YJ Jung, TP

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Circuits and Systems I: Regular Papers, 2005, 52 (12), pp. 2726 - 2738
Issue Date:: 2005-12-01

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Field	Value	Language
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.contributor.author	Wu, RC	en_US
dc.contributor.author	Liang, SF	en_US
dc.contributor.author	Chao, WH	en_US
dc.contributor.author	Chen, YJ	en_US
dc.contributor.author	Jung, TP	en_US
dc.date.issued	2005-12-01	en_US
dc.identifier.citation	IEEE Transactions on Circuits and Systems I: Regular Papers, 2005, 52 (12), pp. 2726 - 2738	en_US
dc.identifier.issn	1057-7122	en_US
dc.identifier.uri	http://hdl.handle.net/10453/123837
dc.description.abstract	Preventing accidents caused by drowsiness has become a major focus of active safety driving in recent years. It requires an optimal technique to continuously detect drivers' cognitive state related to abilities in perception, recognition, and vehicle control in (near-) real-time. The major challenges in developing such a system include: 1) the lack of significant index for detecting drowsiness and 2) complicated and pervasive noise interferences in a realistic and dynamic driving environment. In this paper, we develop a drowsiness-estimation system based on electroencephalogram (EEG) by combining independent component analysis (ICA), power-spectrum analysis, correlation evaluations, and linear regression model to estimate a driver's cognitive state when he/ she drives a car in a virtual reality (VR)-based dynamic simulator. The driving error is defined as deviations between the center of the vehicle and the center of the cruising lane in the lane-keeping driving task. Experimental results demonstrate the feasibility of quantitatively estimating drowsiness level using ICA-based multistream EEG spectra. The proposed ICA-based method applied to power spectrum of ICA components can successfully (1) remove most of EEG artifacts, (2) suggest an optimal montage to place EEG electrodes, and estimate the driver's drowsiness fluctuation indexed by the driving performance measure. Finally, we present a benchmark study in which the accuracy of ICA-component-based alertness estimates compares favorably to scalp-EEG based. © 2005 IEEE.	en_US
dc.relation.ispartof	IEEE Transactions on Circuits and Systems I: Regular Papers	en_US
dc.relation.isbasedon	10.1109/TCSI.2005.857555	en_US
dc.subject.classification	Electrical & Electronic Engineering	en_US
dc.title	EEG-based drowsiness estimation for safety driving using independent component analysis	en_US
dc.type	Journal Article
utslib.citation.volume	12	en_US
utslib.citation.volume	52	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/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	12	en_US
pubs.publication-status	Published	en_US
pubs.volume	52	en_US

Abstract:

Preventing accidents caused by drowsiness has become a major focus of active safety driving in recent years. It requires an optimal technique to continuously detect drivers' cognitive state related to abilities in perception, recognition, and vehicle control in (near-) real-time. The major challenges in developing such a system include: 1) the lack of significant index for detecting drowsiness and 2) complicated and pervasive noise interferences in a realistic and dynamic driving environment. In this paper, we develop a drowsiness-estimation system based on electroencephalogram (EEG) by combining independent component analysis (ICA), power-spectrum analysis, correlation evaluations, and linear regression model to estimate a driver's cognitive state when he/ she drives a car in a virtual reality (VR)-based dynamic simulator. The driving error is defined as deviations between the center of the vehicle and the center of the cruising lane in the lane-keeping driving task. Experimental results demonstrate the feasibility of quantitatively estimating drowsiness level using ICA-based multistream EEG spectra. The proposed ICA-based method applied to power spectrum of ICA components can successfully (1) remove most of EEG artifacts, (2) suggest an optimal montage to place EEG electrodes, and estimate the driver's drowsiness fluctuation indexed by the driving performance measure. Finally, we present a benchmark study in which the accuracy of ICA-component-based alertness estimates compares favorably to scalp-EEG based. © 2005 IEEE.

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http://hdl.handle.net/10453/123837