Real-time vigilance estimation using mobile wireless mindo EEG device with spring-loaded sensors

Ko, LW; Chuang, CH; Huang, CS; Chen, YH; Lu, SW; Liao, LD; Chang, WT; Lin, CT

Real-time vigilance estimation using mobile wireless mindo EEG device with spring-loaded sensors

Ko, LW Chuang, CH

Huang, CS Chen, YH Lu, SW Liao, LD Chang, WT Lin, CT

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013, 8027 LNAI pp. 450 - 458
Issue Date:: 2013-10-07

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Field	Value	Language
dc.contributor.author	Ko, LW	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	Lu, SW	en_US
dc.contributor.author	Liao, LD	en_US
dc.contributor.author	Chang, WT	en_US
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.date.issued	2013-10-07	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013, 8027 LNAI pp. 450 - 458	en_US
dc.identifier.isbn	9783642394539	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120850
dc.description.abstract	Monitoring the neurophysiological activities of human brain dynamics in an operational environment poses a severe measurement challenge using current laboratory-oriented biosensor technology. The goal of this research is to design, develop and test the wearable and wireless dry-electrode EEG human-computer interface (HCI) that can allow assessment of brain activities of participants actively performing ordinary tasks in natural body positions and situations within a real operational environment. Its implications in HCI were demonstrated through a sample application: vigilance-state prediction of participants performing a realistic sustained-attention driving task. Besides, this study further developed an online signal processing for extracting EEG features and assessing cognitive performance. We demonstrated the feasibility of using dry EEG sensors and miniaturized supporting hardware/software to continuously collect EEG data recorded from hairy sites (i.e., occipital region) in a realistic VR-based dynamic driving simulator. © 2013 Springer-Verlag Berlin Heidelberg.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-642-39454-6_48	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Real-time vigilance estimation using mobile wireless mindo EEG device with spring-loaded sensors	en_US
dc.type	Conference Proceeding
utslib.citation.volume	8027 LNAI	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	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
pubs.volume	8027 LNAI	en_US

Abstract:

Monitoring the neurophysiological activities of human brain dynamics in an operational environment poses a severe measurement challenge using current laboratory-oriented biosensor technology. The goal of this research is to design, develop and test the wearable and wireless dry-electrode EEG human-computer interface (HCI) that can allow assessment of brain activities of participants actively performing ordinary tasks in natural body positions and situations within a real operational environment. Its implications in HCI were demonstrated through a sample application: vigilance-state prediction of participants performing a realistic sustained-attention driving task. Besides, this study further developed an online signal processing for extracting EEG features and assessing cognitive performance. We demonstrated the feasibility of using dry EEG sensors and miniaturized supporting hardware/software to continuously collect EEG data recorded from hairy sites (i.e., occipital region) in a realistic VR-based dynamic driving simulator. © 2013 Springer-Verlag Berlin Heidelberg.

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http://hdl.handle.net/10453/120850