Tracking attention based on EEG spectrum

Wang, YK; Jung, TP; Chen, SA; Huang, CS; Lin, CT

Tracking attention based on EEG spectrum

Wang, YK

Jung, TP Chen, SA Huang, CS Lin, CT

Permalink

Publication Type:: Conference Proceeding
Citation:: Communications in Computer and Information Science, 2013, 373 (PART I), pp. 450 - 454
Issue Date:: 2013-01-01

Closed Access

	Filename	Description	Size
	Tracking Attention Based on EEG Spectrum.pdf	Published version	1.36 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Wang, YK https://orcid.org/0000-0001-8390-2664	en_US
dc.contributor.author	Jung, TP	en_US
dc.contributor.author	Chen, SA	en_US
dc.contributor.author	Huang, CS	en_US
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.date.issued	2013-01-01	en_US
dc.identifier.citation	Communications in Computer and Information Science, 2013, 373 (PART I), pp. 450 - 454	en_US
dc.identifier.isbn	9783642394720	en_US
dc.identifier.issn	1865-0929	en_US
dc.identifier.uri	http://hdl.handle.net/10453/88639
dc.description.abstract	Distraction while driving is a serious problem that can have many catastrophic consequences. Developing a countermeasure to detect the drivers' distraction is imperative. This study measured Electroencephalography (EEG) signals from six healthy participants while they were asked to pay their full attention to a lane-keeping driving task or a math problem-solving task. The time courses of six distinct brain networks (Frontal, Central, Parietal, Occipital, Left Motor, and Right Motor) separated by Independent Component Analysis were used to build the distraction-detection model. EEG data were segmented into 400-ms epochs. Across subjects, 80% of the EEG epochs were used to train various classifiers that were tested against the remaining 20% of the data. The classification performance based on support vector machines (SVM) with a radial basis function (RBF) kernel achieved accuracy of 84.7±2.7% or 85.8±1.3% for detecting subjects' focuses of attention to the math-solving or lane-deviation task, respectively. The high attention-detection accuracy demonstrated the feasibility of accurately detecting drivers' attention based on the brain activities. This demonstration may lead to a practical real-time distraction-detection system for improving road safety. © Springer-Verlag Berlin Heidelberg 2013.	en_US
dc.relation.ispartof	Communications in Computer and Information Science	en_US
dc.relation.isbasedon	10.1007/978-3-642-39473-7_90	en_US
dc.title	Tracking attention based on EEG spectrum	en_US
dc.type	Conference Proceeding
utslib.citation.volume	PART I	en_US
utslib.citation.volume	373	en_US
utslib.for	0903 Biomedical 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	PART I	en_US
pubs.publication-status	Published	en_US
pubs.volume	373	en_US

Abstract:

Distraction while driving is a serious problem that can have many catastrophic consequences. Developing a countermeasure to detect the drivers' distraction is imperative. This study measured Electroencephalography (EEG) signals from six healthy participants while they were asked to pay their full attention to a lane-keeping driving task or a math problem-solving task. The time courses of six distinct brain networks (Frontal, Central, Parietal, Occipital, Left Motor, and Right Motor) separated by Independent Component Analysis were used to build the distraction-detection model. EEG data were segmented into 400-ms epochs. Across subjects, 80% of the EEG epochs were used to train various classifiers that were tested against the remaining 20% of the data. The classification performance based on support vector machines (SVM) with a radial basis function (RBF) kernel achieved accuracy of 84.7±2.7% or 85.8±1.3% for detecting subjects' focuses of attention to the math-solving or lane-deviation task, respectively. The high attention-detection accuracy demonstrated the feasibility of accurately detecting drivers' attention based on the brain activities. This demonstration may lead to a practical real-time distraction-detection system for improving road safety. © Springer-Verlag Berlin Heidelberg 2013.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/88639