Real-time assessment of vigilance level using an innovative Mindo4 wireless EEG system

Lin, CT; Chuang, CH; Huang, CS; Chen, YH; Ko, LW

Real-time assessment of vigilance level using an innovative Mindo4 wireless EEG system

Lin, CT

Chuang, CH

Huang, CS Chen, YH Ko, LW

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Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE International Symposium on Circuits and Systems, 2013, pp. 1528 - 1531
Issue Date:: 2013-09-09

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Field	Value	Language
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.contributor.author	Chuang, CH https://orcid.org/0000-0002-5043-8380	en_US
dc.contributor.author	Huang, CS	en_US
dc.contributor.author	Chen, YH	en_US
dc.contributor.author	Ko, LW	en_US
dc.date.issued	2013-09-09	en_US
dc.identifier.citation	Proceedings - IEEE International Symposium on Circuits and Systems, 2013, pp. 1528 - 1531	en_US
dc.identifier.isbn	9781467357609	en_US
dc.identifier.issn	0271-4310	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120103
dc.description.abstract	Monitoring the neurophysiological activities of driver in an operational environment poses a severe measurement challenge using a current laboratory-oriented biosensor technology. The aims of this research are to 1) introduce a dry and wireless EEG system used for conveniently recording EEG signals from forehead regions, 2) propose an effective system for processing EEG recordings and translating them into the vigilance level, and 3) implement the proposed system with a JAVA-based graphical user interface (GUI) for online analysis. To validate the performance of the proposed system, this study recruited eight voluntary subjects to participate a 90-min sustained-attention driving task in a virtual-realistic driving environment. Physiological evidence obtained from the power spectral analysis showed that the dry EEG system could distinguish an alert EEG from a drowsy EEG by evaluating the spectral dynamics of delta and alpha activities. Furthermore, the experimental result of the comparison of the prediction performance using four forehead electrode sites (AF8, FP2, FP1, and AF7) implied that a single-electrode EEG signal used in the mobile and wireless EEG system is able to obtain a high prediction accuracy (∼93%). Taken together, the proposed system applied a dry-EEG device combined with an effective algorithm can be a promising technology for real driving applications. © 2013 IEEE.	en_US
dc.relation.ispartof	Proceedings - IEEE International Symposium on Circuits and Systems	en_US
dc.relation.isbasedon	10.1109/ISCAS.2013.6572149	en_US
dc.title	Real-time assessment of vigilance level using an innovative Mindo4 wireless EEG system	en_US
dc.type	Conference Proceeding
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.publication-status	Published	en_US

Abstract:

Monitoring the neurophysiological activities of driver in an operational environment poses a severe measurement challenge using a current laboratory-oriented biosensor technology. The aims of this research are to 1) introduce a dry and wireless EEG system used for conveniently recording EEG signals from forehead regions, 2) propose an effective system for processing EEG recordings and translating them into the vigilance level, and 3) implement the proposed system with a JAVA-based graphical user interface (GUI) for online analysis. To validate the performance of the proposed system, this study recruited eight voluntary subjects to participate a 90-min sustained-attention driving task in a virtual-realistic driving environment. Physiological evidence obtained from the power spectral analysis showed that the dry EEG system could distinguish an alert EEG from a drowsy EEG by evaluating the spectral dynamics of delta and alpha activities. Furthermore, the experimental result of the comparison of the prediction performance using four forehead electrode sites (AF8, FP2, FP1, and AF7) implied that a single-electrode EEG signal used in the mobile and wireless EEG system is able to obtain a high prediction accuracy (∼93%). Taken together, the proposed system applied a dry-EEG device combined with an effective algorithm can be a promising technology for real driving applications. © 2013 IEEE.

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http://hdl.handle.net/10453/120103