Online Mental Fatigue Monitoring via Indirect Brain Dynamics Evaluation

Pan, Y; Tsang, IW; Lyu, Y; Singh, AK; Lin, C-T

Online Mental Fatigue Monitoring via Indirect Brain Dynamics Evaluation

Pan, Y

Tsang, IW Lyu, Y Singh, AK Lin, C-T

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Publisher:: Massachusetts Institute of Technology Press
Publication Type:: Journal Article
Citation:: Neural Computation, 2021, 33, (6), pp. 1616-1655
Issue Date:: 2021-03-19

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Field	Value	Language
dc.contributor.author	Pan, Y https://orcid.org/0000-0002-7950-4900
dc.contributor.author	Tsang, IW
dc.contributor.author	Lyu, Y
dc.contributor.author	Singh, AK
dc.contributor.author	Lin, C-T
dc.date.accessioned	2022-04-11T03:34:22Z
dc.date.available	2020-12-31
dc.date.available	2022-04-11T03:34:22Z
dc.date.issued	2021-03-19
dc.identifier.citation	Neural Computation, 2021, 33, (6), pp. 1616-1655
dc.identifier.issn	0899-7667
dc.identifier.issn	1530-888X
dc.identifier.uri	http://hdl.handle.net/10453/156048
dc.description.abstract	Driver mental fatigue leads to thousands of traffic accidents. The increasing quality and availability of low-cost electroencephalogram (EEG) systems offer possibilities for practical fatigue monitoring. However, non-data-driven methods, designed for practical, complex situations, usually rely on handcrafted data statistics of EEG signals. To reduce human involvement, we introduce a data-driven methodology for online mental fatigue detection: self-weight ordinal regression (SWORE). Reaction time (RT), referring to the length of time people take to react to an emergency, is widely considered an objective behavioral measure for mental fatigue state. Since regression methods are sensitive to extreme RTs, we propose an indirect RT estimation based on preferences to explore the relationship between EEG and RT, which generalizes to any scenario when an objective fatigue indicator is available. In particular, SWORE evaluates the noisy EEG signals from multiple channels in terms of two states: shaking state and steady state. Modeling the shaking state can discriminate the reliable channels from the uninformative ones, while modeling the steady state can suppress the task-nonrelevant fluctuation within each channel. In addition, an online generalized Bayesian moment matching (online GBMM) algorithm is proposed to online-calibrate SWORE efficiently per participant. Experimental results with 40 participants show that SWORE can maximally achieve consistent with RT, demonstrating the feasibility and adaptability of our proposed framework in practical mental fatigue estimation.
dc.format	Print
dc.language	eng
dc.publisher	Massachusetts Institute of Technology Press
dc.relation	http://purl.org/au-research/grants/arc/DP180100106
dc.relation	http://purl.org/au-research/grants/arc/DP200101328
dc.relation.ispartof	Neural Computation
dc.relation.isbasedon	10.1162/neco_a_01382
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.mesh	Bayes Theorem
dc.subject.mesh	Brain
dc.subject.mesh	Electroencephalography
dc.subject.mesh	Humans
dc.subject.mesh	Mental Fatigue
dc.subject.mesh	Reaction Time
dc.title	Online Mental Fatigue Monitoring via Indirect Brain Dynamics Evaluation
dc.type	Journal Article
utslib.citation.volume	33
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 Computer Science
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2022-04-11T03:34:21Z
pubs.issue	6
pubs.publication-status	Published
pubs.volume	33
utslib.citation.issue	6

Abstract:

Driver mental fatigue leads to thousands of traffic accidents. The increasing quality and availability of low-cost electroencephalogram (EEG) systems offer possibilities for practical fatigue monitoring. However, non-data-driven methods, designed for practical, complex situations, usually rely on handcrafted data statistics of EEG signals. To reduce human involvement, we introduce a data-driven methodology for online mental fatigue detection: self-weight ordinal regression (SWORE). Reaction time (RT), referring to the length of time people take to react to an emergency, is widely considered an objective behavioral measure for mental fatigue state. Since regression methods are sensitive to extreme RTs, we propose an indirect RT estimation based on preferences to explore the relationship between EEG and RT, which generalizes to any scenario when an objective fatigue indicator is available. In particular, SWORE evaluates the noisy EEG signals from multiple channels in terms of two states: shaking state and steady state. Modeling the shaking state can discriminate the reliable channels from the uninformative ones, while modeling the steady state can suppress the task-nonrelevant fluctuation within each channel. In addition, an online generalized Bayesian moment matching (online GBMM) algorithm is proposed to online-calibrate SWORE efficiently per participant. Experimental results with 40 participants show that SWORE can maximally achieve consistent with RT, demonstrating the feasibility and adaptability of our proposed framework in practical mental fatigue estimation.

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http://hdl.handle.net/10453/156048