Increase in brain effective connectivity in multitasking but not in a high-fatigue state

Do, TTN; Wang, YK; Lin, CT

Increase in brain effective connectivity in multitasking but not in a high-fatigue state

Do, TTN Wang, YK Lin, CT

Permalink

Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Cognitive and Developmental Systems, 2020, pp. 1-1
Issue Date:: 2020-01-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

The embargo period expires on 31 Jan 2022

Adobe PDF

Download Accepted manuscriptAdobe PDF (1.16 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Do, TTN
dc.contributor.author	Wang, YK
dc.contributor.author	Lin, CT
dc.date.accessioned	2020-07-23T01:52:20Z
dc.date.available	2020-07-23T01:52:20Z
dc.date.issued	2020-01-01
dc.identifier.citation	IEEE Transactions on Cognitive and Developmental Systems, 2020, pp. 1-1
dc.identifier.issn	2379-8920
dc.identifier.issn	2379-8939
dc.identifier.uri	http://hdl.handle.net/10453/141842
dc.description.abstract	IEEE Multitasking has become omnipresent in daily activities, and increased brain connectivity under high workload conditions has been reported. Moreover, the effect of fatigue on neural activity has been shown in participants performing cognitive tasks, but the effect of fatigue on different cognitive workload conditions is unclear. In this study, we investigated the effect of fatigue on changes in effective connectivity (EC) across the brain network under distinctive workload conditions. There were 133 electroencephalography (EEG) datasets collected from sixteen participants over a five-month study in which high-risk, reduced, and normal states of real-world fatigue were identified through a daily sampling system. The participants were required to perform a lane-keeping task (LKT) with/without multimodal dynamic attention-shifting (DAS) tasks. The results show that the EC magnitude is positively correlated with the increased workload in normal and reduced states. However, low EC was discovered in the high-risk state under high workload condition. To the best of our knowledge, this investigation is the first EEG-based longitudinal study of real-world fatigue under multitasking conditions. These results could be beneficial for real-life applications, and adaptive models are essential for monitoring important brain patterns under varying workload demands and fatigue states.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation	http://purl.org/au-research/grants/arc/DP180100670
dc.relation	http://purl.org/au-research/grants/arc/DP180100656
dc.relation.ispartof	IEEE Transactions on Cognitive and Developmental Systems
dc.relation.isbasedon	10.1109/TCDS.2020.2990898
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.title	Increase in brain effective connectivity in multitasking but not in a high-fatigue state
dc.type	Journal Article
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2022-01-31T00:00:00+1000Z
dc.date.updated	2020-07-23T01:51:50Z
pubs.publication-status	Published

Abstract:

IEEE Multitasking has become omnipresent in daily activities, and increased brain connectivity under high workload conditions has been reported. Moreover, the effect of fatigue on neural activity has been shown in participants performing cognitive tasks, but the effect of fatigue on different cognitive workload conditions is unclear. In this study, we investigated the effect of fatigue on changes in effective connectivity (EC) across the brain network under distinctive workload conditions. There were 133 electroencephalography (EEG) datasets collected from sixteen participants over a five-month study in which high-risk, reduced, and normal states of real-world fatigue were identified through a daily sampling system. The participants were required to perform a lane-keeping task (LKT) with/without multimodal dynamic attention-shifting (DAS) tasks. The results show that the EC magnitude is positively correlated with the increased workload in normal and reduced states. However, low EC was discovered in the high-risk state under high workload condition. To the best of our knowledge, this investigation is the first EEG-based longitudinal study of real-world fatigue under multitasking conditions. These results could be beneficial for real-life applications, and adaptive models are essential for monitoring important brain patterns under varying workload demands and fatigue states.

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

http://hdl.handle.net/10453/141842