Statistical Analysis of Brain Connectivity Estimators during Distracted Driving.

Perera, D; Wang, Y-K; Lin, C-T; Zheng, J; Nguyen, HT; Chai, R

Statistical Analysis of Brain Connectivity Estimators during Distracted Driving.

Perera, D Wang, Y-K Lin, C-T Zheng, J Nguyen, HT Chai, R

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2020, 2020, pp. 3208-3211
Issue Date:: 2020-07

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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	Zheng, J
dc.contributor.author	Nguyen, HT
dc.contributor.author	Chai, R https://orcid.org/0000-0002-1922-7024
dc.date	2020-07-20
dc.date.accessioned	2021-03-15T23:55:08Z
dc.date.available	2021-03-15T23:55:08Z
dc.date.issued	2020-07
dc.identifier.citation	Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2020, 2020, pp. 3208-3211
dc.identifier.isbn	9781728119908
dc.identifier.issn	2375-7477
dc.identifier.issn	2694-0604
dc.identifier.uri	http://hdl.handle.net/10453/147193
dc.description.abstract	This paper presents comparison of brain connectivity estimators of distracted drivers and non-distracted drivers based on statistical analysis. Twelve healthy volunteers with more than one year of driving experience participated in this experiment. Lane-keeping tasks and the Math problem-solving task were introduced in the experiment and EEGs (electroencephalogram) were used to record the brain waves. Granger-Geweke causality (GGC), directed transfer function (DTF) and partial directed coherence (PDC) brain connectivity estimation methods were used in brain connectivity analysis. Correlation test and a student's t-test were conducted on the connectivity matrixes. Results show a significant difference between the mean of distracted drivers and non-distracted driver's brain connectivity matrixes. GGC and DTF methods student's t-tests shows a p-value below 0.05 with the correlation coefficients varying from 0.62 to 0.38. PDC connectivity estimation method does not show a significant difference between the connectivity matrixes means unless it is compared with lane keeping task and the normal driving task. Furthermore, it shows a strong positive correlation between the connectivity matrixes.
dc.format	Print
dc.language	en
dc.publisher	IEEE
dc.relation	University of Technology Sydney
dc.relation.ispartof	Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
dc.relation.ispartof	2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society
dc.relation.isbasedon	10.1109/embc44109.2020.9176240
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject.mesh	Brain
dc.subject.mesh	Humans
dc.subject.mesh	Electroencephalography
dc.subject.mesh	Brain Mapping
dc.subject.mesh	Brain Waves
dc.subject.mesh	Distracted Driving
dc.subject.mesh	Brain
dc.subject.mesh	Brain Mapping
dc.subject.mesh	Brain Waves
dc.subject.mesh	Distracted Driving
dc.subject.mesh	Electroencephalography
dc.subject.mesh	Humans
dc.title	Statistical Analysis of Brain Connectivity Estimators during Distracted Driving.
dc.type	Conference Proceeding
utslib.citation.volume	2020
utslib.location.activity	United States
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
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2022-07-24T00:00:00+1000Z
dc.date.updated	2021-03-15T23:55:06Z
pubs.finish-date	2020-07-24
pubs.publication-status	Published
pubs.start-date	2020-07-20
pubs.volume	2020

Abstract:

This paper presents comparison of brain connectivity estimators of distracted drivers and non-distracted drivers based on statistical analysis. Twelve healthy volunteers with more than one year of driving experience participated in this experiment. Lane-keeping tasks and the Math problem-solving task were introduced in the experiment and EEGs (electroencephalogram) were used to record the brain waves. Granger-Geweke causality (GGC), directed transfer function (DTF) and partial directed coherence (PDC) brain connectivity estimation methods were used in brain connectivity analysis. Correlation test and a student's t-test were conducted on the connectivity matrixes. Results show a significant difference between the mean of distracted drivers and non-distracted driver's brain connectivity matrixes. GGC and DTF methods student's t-tests shows a p-value below 0.05 with the correlation coefficients varying from 0.62 to 0.38. PDC connectivity estimation method does not show a significant difference between the connectivity matrixes means unless it is compared with lane keeping task and the normal driving task. Furthermore, it shows a strong positive correlation between the connectivity matrixes.

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http://hdl.handle.net/10453/147193