EEG-based prediction of driver's cognitive performance by deep convolutional neural network

Hajinoroozi, M; Mao, Z; Jung, TP; Lin, CT; Huang, Y

EEG-based prediction of driver's cognitive performance by deep convolutional neural network

Hajinoroozi, M Mao, Z Jung, TP Lin, CT

Huang, Y

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Publication Type:: Journal Article
Citation:: Signal Processing: Image Communication, 2016, 47 pp. 549 - 555
Issue Date:: 2016-09-01

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Field	Value	Language
dc.contributor.author	Hajinoroozi, M	en_US
dc.contributor.author	Mao, Z	en_US
dc.contributor.author	Jung, TP	en_US
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.contributor.author	Huang, Y	en_US
dc.date.issued	2016-09-01	en_US
dc.identifier.citation	Signal Processing: Image Communication, 2016, 47 pp. 549 - 555	en_US
dc.identifier.issn	0923-5965	en_US
dc.identifier.uri	http://hdl.handle.net/10453/113462
dc.description.abstract	© 2016 Elsevier B.V. We considered the prediction of driver's cognitive states related to driving performance using EEG signals. We proposed a novel channel-wise convolutional neural network (CCNN) whose architecture considers the unique characteristics of EEG data. We also discussed CCNN-R, a CCNN variation that uses Restricted Boltzmann Machine to replace the convolutional filter, and derived the detailed algorithm. To test the performance of CCNN and CCNN-R, we assembled a large EEG dataset from 3 studies of driver fatigue that includes samples from 37 subjects. Using this dataset, we investigated the new CCNN and CCNN-R on raw EEG data and also Independent Component Analysis (ICA) decomposition. We tested both within-subject and cross-subject predictions and the results showed CCNN and CCNN-R achieved robust and improved performance over conventional DNN and CNN as well as other non-DL algorithms.	en_US
dc.relation.ispartof	Signal Processing: Image Communication	en_US
dc.relation.isbasedon	10.1016/j.image.2016.05.018	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	EEG-based prediction of driver's cognitive performance by deep convolutional neural network	en_US
dc.type	Journal Article
utslib.citation.volume	47	en_US
utslib.for	0906 Electrical and Electronic Engineering	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
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	47	en_US

Abstract:

© 2016 Elsevier B.V. We considered the prediction of driver's cognitive states related to driving performance using EEG signals. We proposed a novel channel-wise convolutional neural network (CCNN) whose architecture considers the unique characteristics of EEG data. We also discussed CCNN-R, a CCNN variation that uses Restricted Boltzmann Machine to replace the convolutional filter, and derived the detailed algorithm. To test the performance of CCNN and CCNN-R, we assembled a large EEG dataset from 3 studies of driver fatigue that includes samples from 37 subjects. Using this dataset, we investigated the new CCNN and CCNN-R on raw EEG data and also Independent Component Analysis (ICA) decomposition. We tested both within-subject and cross-subject predictions and the results showed CCNN and CCNN-R achieved robust and improved performance over conventional DNN and CNN as well as other non-DL algorithms.

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

http://hdl.handle.net/10453/113462