Improving EEG-Based Driver Distraction Classification Using Brain Connectivity Estimators.

Perera, D; Wang, Y-K; Lin, C-T; Nguyen, H; Chai, R

Improving EEG-Based Driver Distraction Classification Using Brain Connectivity Estimators.

Perera, D Wang, Y-K Lin, C-T Nguyen, H Chai, R

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Sensors (Basel), 2022, 22, (16), pp. 6230
Issue Date:: 2022-08-19

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Field	Value	Language
dc.contributor.author	Perera, D
dc.contributor.author	Wang, Y-K
dc.contributor.author	Lin, C-T
dc.contributor.author	Nguyen, H
dc.contributor.author	Chai, R https://orcid.org/0000-0002-1922-7024
dc.date.accessioned	2022-12-07T01:42:55Z
dc.date.available	2022-08-15
dc.date.available	2022-12-07T01:42:55Z
dc.date.issued	2022-08-19
dc.identifier.citation	Sensors (Basel), 2022, 22, (16), pp. 6230
dc.identifier.issn	1424-8220
dc.identifier.issn	1424-8220
dc.identifier.uri	http://hdl.handle.net/10453/164175
dc.description.abstract	This paper discusses a novel approach to an EEG (electroencephalogram)-based driver distraction classification by using brain connectivity estimators as features. Ten healthy volunteers with more than one year of driving experience and an average age of 24.3 participated in a virtual reality environment with two conditions, a simple math problem-solving task and a lane-keeping task to mimic the distracted driving task and a non-distracted driving task, respectively. Independent component analysis (ICA) was conducted on the selected epochs of six selected components relevant to the frontal, central, parietal, occipital, left motor, and right motor areas. Granger-Geweke causality (GGC), directed transfer function (DTF), partial directed coherence (PDC), and generalized partial directed coherence (GPDC) brain connectivity estimators were used to calculate the connectivity matrixes. These connectivity matrixes were used as features to train the support vector machine (SVM) with the radial basis function (RBF) and classify the distracted and non-distracted driving tasks. GGC, DTF, PDC, and GPDC connectivity estimators yielded the classification accuracies of 82.27%, 70.02%, 86.19%, and 80.95%, respectively. Further analysis of the PDC connectivity estimator was conducted to determine the best window to differentiate between the distracted and non-distracted driving tasks. This study suggests that the PDC connectivity estimator can yield better classification accuracy for driver distractions.
dc.format	Electronic
dc.language	eng
dc.publisher	MDPI
dc.relation.ispartof	Sensors (Basel)
dc.relation.isbasedon	10.3390/s22166230
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0301 Analytical Chemistry, 0502 Environmental Science and Management, 0602 Ecology, 0805 Distributed Computing, 0906 Electrical and Electronic Engineering
dc.subject.classification	Analytical Chemistry
dc.subject.mesh	Adult
dc.subject.mesh	Automobile Driving
dc.subject.mesh	Brain
dc.subject.mesh	Brain Mapping
dc.subject.mesh	Distracted Driving
dc.subject.mesh	Electroencephalography
dc.subject.mesh	Humans
dc.subject.mesh	Motor Cortex
dc.subject.mesh	Young Adult
dc.subject.mesh	Brain
dc.subject.mesh	Motor Cortex
dc.subject.mesh	Humans
dc.subject.mesh	Electroencephalography
dc.subject.mesh	Brain Mapping
dc.subject.mesh	Automobile Driving
dc.subject.mesh	Adult
dc.subject.mesh	Young Adult
dc.subject.mesh	Distracted Driving
dc.title	Improving EEG-Based Driver Distraction Classification Using Brain Connectivity Estimators.
dc.type	Journal Article
utslib.citation.volume	22
utslib.location.activity	Switzerland
utslib.for	0301 Analytical Chemistry
utslib.for	0502 Environmental Science and Management
utslib.for	0602 Ecology
utslib.for	0805 Distributed Computing
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 Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Centre for Health Technologies (CHT)
utslib.copyright.status	open_access	*
dc.date.updated	2022-12-07T01:42:39Z
pubs.issue	16
pubs.publication-status	Published online
pubs.volume	22
utslib.citation.issue	16

Abstract:

This paper discusses a novel approach to an EEG (electroencephalogram)-based driver distraction classification by using brain connectivity estimators as features. Ten healthy volunteers with more than one year of driving experience and an average age of 24.3 participated in a virtual reality environment with two conditions, a simple math problem-solving task and a lane-keeping task to mimic the distracted driving task and a non-distracted driving task, respectively. Independent component analysis (ICA) was conducted on the selected epochs of six selected components relevant to the frontal, central, parietal, occipital, left motor, and right motor areas. Granger-Geweke causality (GGC), directed transfer function (DTF), partial directed coherence (PDC), and generalized partial directed coherence (GPDC) brain connectivity estimators were used to calculate the connectivity matrixes. These connectivity matrixes were used as features to train the support vector machine (SVM) with the radial basis function (RBF) and classify the distracted and non-distracted driving tasks. GGC, DTF, PDC, and GPDC connectivity estimators yielded the classification accuracies of 82.27%, 70.02%, 86.19%, and 80.95%, respectively. Further analysis of the PDC connectivity estimator was conducted to determine the best window to differentiate between the distracted and non-distracted driving tasks. This study suggests that the PDC connectivity estimator can yield better classification accuracy for driver distractions.

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http://hdl.handle.net/10453/164175