The Coherence of the Working Memory Study Between Deep Neural Networks and Neurophysiology: Insights From Distinguishing Topographical Electroencephalogram Data Under Different Workloads

Ming, Y Lin, C-T

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Systems Man and Cybernetics Magazine, 2021, 7, (4), pp. 24-30
Issue Date:: 2021-10-01

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Field	Value	Language
dc.contributor.author	Ming, Y
dc.contributor.author	Lin, C-T
dc.date.accessioned	2022-09-15T20:10:08Z
dc.date.available	2022-09-15T20:10:08Z
dc.date.issued	2021-10-01
dc.identifier.citation	IEEE Systems Man and Cybernetics Magazine, 2021, 7, (4), pp. 24-30
dc.identifier.issn	2380-1298
dc.identifier.issn	2333-942X
dc.identifier.uri	http://hdl.handle.net/10453/161840
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Systems Man and Cybernetics Magazine
dc.relation.isbasedon	10.1109/msmc.2021.3090569
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	The Coherence of the Working Memory Study Between Deep Neural Networks and Neurophysiology: Insights From Distinguishing Topographical Electroencephalogram Data Under Different Workloads
dc.type	Journal Article
utslib.citation.volume	7
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	*
utslib.copyright.embargo	2023-10-31T00:00:00+1000Z
dc.date.updated	2022-09-15T20:10:06Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	7
utslib.citation.issue	4

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