EEG-Based Attention Tracking During Distracted Driving.

Wang, Y-K; Jung, T-P; Lin, C-T

EEG-Based Attention Tracking During Distracted Driving.

Wang, Y-K Jung, T-P Lin, C-T

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Trans Neural Syst Rehabil Eng, 2015, 23, (6), pp. 1085-1094
Issue Date:: 2015-11

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Field	Value	Language
dc.contributor.author	Wang, Y-K
dc.contributor.author	Jung, T-P
dc.contributor.author	Lin, C-T
dc.date.accessioned	2024-03-31T15:36:11Z
dc.date.available	2024-03-31T15:36:11Z
dc.date.issued	2015-11
dc.identifier.citation	IEEE Trans Neural Syst Rehabil Eng, 2015, 23, (6), pp. 1085-1094
dc.identifier.issn	1534-4320
dc.identifier.issn	1558-0210
dc.identifier.uri	http://hdl.handle.net/10453/177375
dc.description.abstract	Distracted driving might lead to many catastrophic consequences. Developing a countermeasure to track drivers' focus of attention (FOA) and engagement of operators in dual (multi)-tasking conditions is thus imperative. Ten healthy volunteers participated in a dual-task experiment that comprised two tasks: a lane-keeping driving task and a mathematical problem-solving task (e.g., 24+15=37?) during which their electroencephalogram (EEG) and behaviors were concurrently recorded. Independent component analysis (ICA) was employed as a spatial filter to separate the contributions of independent sources from the recorded EEG data. The power spectra of six components (i.e., frontal, central, parietal, occipital, left motor, and right motor) extracted from single-task conditions were fed into support vector machine (SVM) based on the radial basis function (RBF) kernel to build an FOA assessment system. The system achieved 84.6±5.8% and 86.2±5.4% classification accuracies in detecting the participants' FOAs on the math versus driving tasks, respectively. This FOA assessment system was then applied to evaluate participants' FOAs during dual-task conditions. The detected FOAs revealed that participants' cognitive attention and strategies dynamically changed between tasks to optimize the overall performance, as attention was limited and competed. The empirical results of this study demonstrate the feasibility of a practical system to continuously estimating cognitive attention through EEG spectra.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Trans Neural Syst Rehabil Eng
dc.relation.isbasedon	10.1109/TNSRE.2015.2415520
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0903 Biomedical Engineering, 0906 Electrical and Electronic Engineering
dc.subject.classification	Biomedical Engineering
dc.subject.classification	4003 Biomedical engineering
dc.subject.classification	4007 Control engineering, mechatronics and robotics
dc.subject.mesh	Adult
dc.subject.mesh	Attention
dc.subject.mesh	Automobile Driving
dc.subject.mesh	Brain
dc.subject.mesh	Cognition
dc.subject.mesh	Electroencephalography
dc.subject.mesh	Healthy Volunteers
dc.subject.mesh	Humans
dc.subject.mesh	Male
dc.subject.mesh	Problem Solving
dc.subject.mesh	Psychomotor Performance
dc.subject.mesh	Reaction Time
dc.subject.mesh	Support Vector Machine
dc.subject.mesh	Visual Perception
dc.subject.mesh	Young Adult
dc.subject.mesh	Brain
dc.subject.mesh	Humans
dc.subject.mesh	Electroencephalography
dc.subject.mesh	Cognition
dc.subject.mesh	Problem Solving
dc.subject.mesh	Visual Perception
dc.subject.mesh	Psychomotor Performance
dc.subject.mesh	Attention
dc.subject.mesh	Reaction Time
dc.subject.mesh	Automobile Driving
dc.subject.mesh	Adult
dc.subject.mesh	Male
dc.subject.mesh	Young Adult
dc.subject.mesh	Healthy Volunteers
dc.subject.mesh	Support Vector Machine
dc.subject.mesh	Adult
dc.subject.mesh	Attention
dc.subject.mesh	Automobile Driving
dc.subject.mesh	Brain
dc.subject.mesh	Cognition
dc.subject.mesh	Electroencephalography
dc.subject.mesh	Healthy Volunteers
dc.subject.mesh	Humans
dc.subject.mesh	Male
dc.subject.mesh	Problem Solving
dc.subject.mesh	Psychomotor Performance
dc.subject.mesh	Reaction Time
dc.subject.mesh	Support Vector Machine
dc.subject.mesh	Visual Perception
dc.subject.mesh	Young Adult
dc.title	EEG-Based Attention Tracking During Distracted Driving.
dc.type	Journal Article
utslib.citation.volume	23
utslib.location.activity	United States
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0903 Biomedical Engineering
utslib.for	0903 Biomedical Engineering
utslib.for	0906 Electrical and Electronic Engineering
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/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2024-03-31T15:36:08Z
pubs.issue	6
pubs.publication-status	Published
pubs.volume	23
utslib.citation.issue	6

Abstract:

Distracted driving might lead to many catastrophic consequences. Developing a countermeasure to track drivers' focus of attention (FOA) and engagement of operators in dual (multi)-tasking conditions is thus imperative. Ten healthy volunteers participated in a dual-task experiment that comprised two tasks: a lane-keeping driving task and a mathematical problem-solving task (e.g., 24+15=37?) during which their electroencephalogram (EEG) and behaviors were concurrently recorded. Independent component analysis (ICA) was employed as a spatial filter to separate the contributions of independent sources from the recorded EEG data. The power spectra of six components (i.e., frontal, central, parietal, occipital, left motor, and right motor) extracted from single-task conditions were fed into support vector machine (SVM) based on the radial basis function (RBF) kernel to build an FOA assessment system. The system achieved 84.6±5.8% and 86.2±5.4% classification accuracies in detecting the participants' FOAs on the math versus driving tasks, respectively. This FOA assessment system was then applied to evaluate participants' FOAs during dual-task conditions. The detected FOAs revealed that participants' cognitive attention and strategies dynamically changed between tasks to optimize the overall performance, as attention was limited and competed. The empirical results of this study demonstrate the feasibility of a practical system to continuously estimating cognitive attention through EEG spectra.

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

http://hdl.handle.net/10453/177375