Developing an EEG-based on-line closed-loop lapse detection and mitigation system

Wang, YT; Huang, KC; Wei, CS; Huang, TY; Ko, LW; Lin, CT; Cheng, CK; Jung, TP

Developing an EEG-based on-line closed-loop lapse detection and mitigation system

Wang, YT Huang, KC Wei, CS Huang, TY Ko, LW Lin, CT

Cheng, CK Jung, TP

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Publication Type:: Journal Article
Citation:: Frontiers in Neuroscience, 2014, 8 (OCT)
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Wang, YT	en_US
dc.contributor.author	Huang, KC	en_US
dc.contributor.author	Wei, CS	en_US
dc.contributor.author	Huang, TY	en_US
dc.contributor.author	Ko, LW	en_US
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.contributor.author	Cheng, CK	en_US
dc.contributor.author	Jung, TP	en_US
dc.date.available	2014-09-24	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Frontiers in Neuroscience, 2014, 8 (OCT)	en_US
dc.identifier.issn	1662-4548	en_US
dc.identifier.uri	http://hdl.handle.net/10453/118211
dc.description.abstract	© 2014 Wang, Huang, Wei, Huang, Ko, Lin, Cheng and Jung. In America, 60% of adults reported that they have driven a motor vehicle while feeling drowsy, and at least 15-20% of fatal car accidents are fatigue-related. This study translates previous laboratory-oriented neurophysiological research to design, develop, and test an On-line Closed-loop Lapse Detection and Mitigation (OCLDM) System featuring a mobile wireless dry-sensor EEG headgear and a cell-phone based real-time EEG processing platform. Eleven subjects participated in an event-related lane-keeping task, in which they were instructed to manipulate a randomly deviated, fixed-speed cruising car on a 4-lane highway. This was simulated in a 1st person view with an 8-screen and 8-projector immersive virtual-reality environment. When the subjects experienced lapses or failed to respond to events during the experiment, auditory warning was delivered to rectify the performance decrements. However, the arousing auditory signals were not always effective. The EEG spectra exhibited statistically significant differences between effective and ineffective arousing signals, suggesting that EEG spectra could be used as a countermeasure of the efficacy of arousing signals. In this on-line pilot study, the proposed OCLDM System was able to continuously detect EEG signatures of fatigue, deliver arousing warning to subjects suffering momentary cognitive lapses, and assess the efficacy of the warning in near real-time to rectify cognitive lapses. The on-line testing results of the OCLDM System validated the efficacy of the arousing signals in improving subjects' response times to the subsequent lane-departure events. This study may lead to a practical on-line lapse detection and mitigation system in real-world environments.	en_US
dc.relation.ispartof	Frontiers in Neuroscience	en_US
dc.relation.isbasedon	10.3389/fnins.2014.00321	en_US
dc.title	Developing an EEG-based on-line closed-loop lapse detection and mitigation system	en_US
dc.type	Journal Article
utslib.citation.volume	OCT	en_US
utslib.citation.volume	8	en_US
utslib.for	1109 Neurosciences	en_US
utslib.for	1702 Cognitive Sciences	en_US
utslib.for	1701 Psychology	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	open_access
pubs.issue	OCT	en_US
pubs.publication-status	Published	en_US
pubs.volume	8	en_US

Abstract:

© 2014 Wang, Huang, Wei, Huang, Ko, Lin, Cheng and Jung. In America, 60% of adults reported that they have driven a motor vehicle while feeling drowsy, and at least 15-20% of fatal car accidents are fatigue-related. This study translates previous laboratory-oriented neurophysiological research to design, develop, and test an On-line Closed-loop Lapse Detection and Mitigation (OCLDM) System featuring a mobile wireless dry-sensor EEG headgear and a cell-phone based real-time EEG processing platform. Eleven subjects participated in an event-related lane-keeping task, in which they were instructed to manipulate a randomly deviated, fixed-speed cruising car on a 4-lane highway. This was simulated in a 1st person view with an 8-screen and 8-projector immersive virtual-reality environment. When the subjects experienced lapses or failed to respond to events during the experiment, auditory warning was delivered to rectify the performance decrements. However, the arousing auditory signals were not always effective. The EEG spectra exhibited statistically significant differences between effective and ineffective arousing signals, suggesting that EEG spectra could be used as a countermeasure of the efficacy of arousing signals. In this on-line pilot study, the proposed OCLDM System was able to continuously detect EEG signatures of fatigue, deliver arousing warning to subjects suffering momentary cognitive lapses, and assess the efficacy of the warning in near real-time to rectify cognitive lapses. The on-line testing results of the OCLDM System validated the efficacy of the arousing signals in improving subjects' response times to the subsequent lane-departure events. This study may lead to a practical on-line lapse detection and mitigation system in real-world environments.

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http://hdl.handle.net/10453/118211