Stochastic Multichannel Ranking with Brain Dynamics Preferences.

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

Stochastic Multichannel Ranking with Brain Dynamics Preferences.

Pan, Y Tsang, IW Singh, AK Lin, C-T Sugiyama, M

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Publisher:: MIT Press - Journals
Publication Type:: Journal Article
Citation:: Neural computation, 2020, pp. 1-32
Issue Date:: 2020-06-10

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Field	Value	Language
dc.contributor.author	Pan, Y
dc.contributor.author	Tsang, IW
dc.contributor.author	Singh, AK
dc.contributor.author	Lin, C-T
dc.contributor.author	Sugiyama, M
dc.date.accessioned	2020-07-13T07:24:11Z
dc.date.available	2020-12-10T18:06:32Z
dc.date.issued	2020-06-10
dc.identifier.citation	Neural computation, 2020, pp. 1-32
dc.identifier.issn	0899-7667
dc.identifier.issn	1530-888X
dc.identifier.uri	http://hdl.handle.net/10453/141771
dc.description.abstract	A driver's cognitive state of mental fatigue significantly affects his or her driving performance and more important, public safety. Previous studies have leveraged reaction time (RT) as the metric for mental fatigue and aim at estimating the exact value of RT using electroencephalogram (EEG) signals within a regression model. However, due to the easily corrupted and also nonsmooth properties of RTs during data collection, methods focusing on predicting the exact value of a noisy measurement, RT generally suffer from poor generalization performance. Considering that human RT is the reflection of brain dynamics preference (BDP) rather than a single regression output of EEG signals, we propose a novel channel-reliability-aware ranking (CArank) model for the multichannel ranking problem. CArank learns from BDPs using EEG data robustly and aims at preserving the ordering corresponding to RTs. In particular, we introduce a transition matrix to characterize the reliability of each channel used in the EEG data, which helps in learning with BDPs only from informative EEG channels. To handle large-scale EEG signals, we propose a stochastic-generalized expectation maximum (SGEM) algorithm to update CArank in an online fashion. Comprehensive empirical analysis on EEG signals from 40 participants shows that our CArank achieves substantial improvements in reliability while simultaneously detecting noisy or less informative EEG channels.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	MIT Press - Journals
dc.relation.ispartof	Neural computation
dc.relation.isbasedon	10.1162/neco_a_01293
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Stochastic Multichannel Ranking with Brain Dynamics Preferences.
dc.type	Journal Article
utslib.location.activity	United States
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	*
dc.date.updated	2020-07-13T07:24:05Z
pubs.publication-status	Published

Abstract:

A driver's cognitive state of mental fatigue significantly affects his or her driving performance and more important, public safety. Previous studies have leveraged reaction time (RT) as the metric for mental fatigue and aim at estimating the exact value of RT using electroencephalogram (EEG) signals within a regression model. However, due to the easily corrupted and also nonsmooth properties of RTs during data collection, methods focusing on predicting the exact value of a noisy measurement, RT generally suffer from poor generalization performance. Considering that human RT is the reflection of brain dynamics preference (BDP) rather than a single regression output of EEG signals, we propose a novel channel-reliability-aware ranking (CArank) model for the multichannel ranking problem. CArank learns from BDPs using EEG data robustly and aims at preserving the ordering corresponding to RTs. In particular, we introduce a transition matrix to characterize the reliability of each channel used in the EEG data, which helps in learning with BDPs only from informative EEG channels. To handle large-scale EEG signals, we propose a stochastic-generalized expectation maximum (SGEM) algorithm to update CArank in an online fashion. Comprehensive empirical analysis on EEG signals from 40 participants shows that our CArank achieves substantial improvements in reliability while simultaneously detecting noisy or less informative EEG channels.

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http://hdl.handle.net/10453/141771