Sustained attention driving task analysis based on recurrent residual neural network using EEG data

Ming, Y; Wang, YK; Prasad, M; Wu, D; Lin, CT

Sustained attention driving task analysis based on recurrent residual neural network using EEG data

Ming, Y

Wang, YK

Prasad, M

Wu, D Lin, CT

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Publication Type:: Conference Proceeding
Citation:: IEEE International Conference on Fuzzy Systems, 2018, 2018-July
Issue Date:: 2018-10-12

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Field	Value	Language
dc.contributor.author	Ming, Y https://orcid.org/0000-0003-3837-5580	en_US
dc.contributor.author	Wang, YK https://orcid.org/0000-0001-8390-2664	en_US
dc.contributor.author	Prasad, M https://orcid.org/0000-0001-9719-2253	en_US
dc.contributor.author	Wu, D	en_US
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.date.available	2020-10-16T18:02:13Z
dc.date.issued	2018-10-12	en_US
dc.identifier.citation	IEEE International Conference on Fuzzy Systems, 2018, 2018-July	en_US
dc.identifier.isbn	9781509060207	en_US
dc.identifier.issn	1098-7584	en_US
dc.identifier.uri	http://hdl.handle.net/10453/129273
dc.description.abstract	© 2018 IEEE. This paper proposes applying recurrent residual network (RRN) for analyzing electroencephalogram (EEG) data captured during a simulated sustained attention driving task. We first address the suitableness of utilizing residual structure as well as adopting recurrent structure for EEG signal processing. Then based on these descriptions a recurrent residual network is tailored and depicted in detail. Thirdly we use an EEG dataset obtained from a sustained-attention experiment for our model justification. By applying the RRN model to the experimental data and via the competitive result achieved, we demonstrate the elegance of the proposed model. At last, we discuss the characteristics of the learned filters and their interpretations from EEG frequency band perspectives.	en_US
dc.relation.ispartof	IEEE International Conference on Fuzzy Systems	en_US
dc.relation.isbasedon	10.1109/FUZZ-IEEE.2018.8491646	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Sustained attention driving task analysis based on recurrent residual neural network using EEG data	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2018-July	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	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
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	2018-July	en_US

Abstract:

© 2018 IEEE. This paper proposes applying recurrent residual network (RRN) for analyzing electroencephalogram (EEG) data captured during a simulated sustained attention driving task. We first address the suitableness of utilizing residual structure as well as adopting recurrent structure for EEG signal processing. Then based on these descriptions a recurrent residual network is tailored and depicted in detail. Thirdly we use an EEG dataset obtained from a sustained-attention experiment for our model justification. By applying the RRN model to the experimental data and via the competitive result achieved, we demonstrate the elegance of the proposed model. At last, we discuss the characteristics of the learned filters and their interpretations from EEG frequency band perspectives.

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

http://hdl.handle.net/10453/128239