Brain network changes in fatigued drivers: A longitudinal study in a real-world environment based on the effective connectivity analysis and actigraphy data

Fonseca, A; Kerick, S; King, JT; Lin, CT; Jung, TP

Brain network changes in fatigued drivers: A longitudinal study in a real-world environment based on the effective connectivity analysis and actigraphy data

Fonseca, A Kerick, S King, JT Lin, CT

Jung, TP

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Publication Type:: Journal Article
Citation:: Frontiers in Human Neuroscience, 2018, 12
Issue Date:: 2018-11-12

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Field	Value	Language
dc.contributor.author	Fonseca, A	en_US
dc.contributor.author	Kerick, S	en_US
dc.contributor.author	King, JT	en_US
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.contributor.author	Jung, TP	en_US
dc.date.available	2018-09-27	en_US
dc.date.issued	2018-11-12	en_US
dc.identifier.citation	Frontiers in Human Neuroscience, 2018, 12	en_US
dc.identifier.uri	http://hdl.handle.net/10453/130187
dc.description.abstract	© 2018 Fonseca, Kerick, King, Lin and Jung. The analysis of neurophysiological changes during driving can clarify the mechanisms of fatigue, considered an important cause of vehicle accidents. The fluctuations in alertness can be investigated as changes in the brain network connections, reflected in the direction and magnitude of the information transferred. Those changes are induced not only by the time on task but also by the quality of sleep. In an unprecedented 5-month longitudinal study, daily sampling actigraphy and EEG data were collected during a sustained-attention driving task within a near-real-world environment. Using a performance index associated with the subjects' reaction times and a predictive score related to the sleep quality, we identify fatigue levels in drivers and investigate the shifts in their effective connectivity in different frequency bands, through the analysis of the dynamical coupling between brain areas. Study results support the hypothesis that combining EEG, behavioral and actigraphy data can reveal new features of the decline in alertness. In addition, the use of directed measures such as the Convergent Cross Mapping can contribute to the development of fatigue countermeasure devices.	en_US
dc.relation.ispartof	Frontiers in Human Neuroscience	en_US
dc.relation.isbasedon	10.3389/fnhum.2018.00418	en_US
dc.subject.classification	Experimental Psychology	en_US
dc.title	Brain network changes in fatigued drivers: A longitudinal study in a real-world environment based on the effective connectivity analysis and actigraphy data	en_US
dc.type	Journal Article
utslib.citation.volume	12	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	1109 Neurosciences	en_US
utslib.for	1701 Psychology	en_US
utslib.for	1702 Cognitive Sciences	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.publication-status	Published	en_US
pubs.volume	12	en_US

Abstract:

© 2018 Fonseca, Kerick, King, Lin and Jung. The analysis of neurophysiological changes during driving can clarify the mechanisms of fatigue, considered an important cause of vehicle accidents. The fluctuations in alertness can be investigated as changes in the brain network connections, reflected in the direction and magnitude of the information transferred. Those changes are induced not only by the time on task but also by the quality of sleep. In an unprecedented 5-month longitudinal study, daily sampling actigraphy and EEG data were collected during a sustained-attention driving task within a near-real-world environment. Using a performance index associated with the subjects' reaction times and a predictive score related to the sleep quality, we identify fatigue levels in drivers and investigate the shifts in their effective connectivity in different frequency bands, through the analysis of the dynamical coupling between brain areas. Study results support the hypothesis that combining EEG, behavioral and actigraphy data can reveal new features of the decline in alertness. In addition, the use of directed measures such as the Convergent Cross Mapping can contribute to the development of fatigue countermeasure devices.

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http://hdl.handle.net/10453/130187