Image-based EEG signal processing for driving fatigue prediction

Cheng, EJ; Young, KY; Lin, CT

Image-based EEG signal processing for driving fatigue prediction

Cheng, EJ Young, KY Lin, CT

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Publication Type:: Conference Proceeding
Citation:: 2018 International Automatic Control Conference, CACS 2018, 2019
Issue Date:: 2019-01-09

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Field	Value	Language
dc.contributor.author	Cheng, EJ	en_US
dc.contributor.author	Young, KY	en_US
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.date.issued	2019-01-09	en_US
dc.identifier.citation	2018 International Automatic Control Conference, CACS 2018, 2019	en_US
dc.identifier.isbn	9781538662786	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132946
dc.description.abstract	© 2018 IEEE. This study proposes a EEG-based prediction system that transform the measured EEG record into an image-liked data for estimating the drowsiness level of drivers. Drowsy driving is one of the main factors to the occurrence of traffic accident. Since drivers themselves may not always immediately recognize that they are in the drowsy state, the risk of traffic accident increases while the driver is in the low vigilance state. In order to address this problem, the estimation of drowsy driving state via brain-computer interfaces (BCI) becomes a major concern in the driving safety field. This study transforms the measured EEG record into a image-liked feature maps, and then passes these feature maps to a Convolutional Neural Network (CNN) to learn the discriminative representations. The proposed drowsiness prediction system is evaluated by leave-one-subject-out cross-validation. The results indicate that our approach provides impressive and robust prediction performance on the EEG dataset without artifact removal process.	en_US
dc.relation.ispartof	2018 International Automatic Control Conference, CACS 2018	en_US
dc.relation.isbasedon	10.1109/CACS.2018.8606734	en_US
dc.title	Image-based EEG signal processing for driving fatigue prediction	en_US
dc.type	Conference Proceeding
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

Abstract:

© 2018 IEEE. This study proposes a EEG-based prediction system that transform the measured EEG record into an image-liked data for estimating the drowsiness level of drivers. Drowsy driving is one of the main factors to the occurrence of traffic accident. Since drivers themselves may not always immediately recognize that they are in the drowsy state, the risk of traffic accident increases while the driver is in the low vigilance state. In order to address this problem, the estimation of drowsy driving state via brain-computer interfaces (BCI) becomes a major concern in the driving safety field. This study transforms the measured EEG record into a image-liked feature maps, and then passes these feature maps to a Convolutional Neural Network (CNN) to learn the discriminative representations. The proposed drowsiness prediction system is evaluated by leave-one-subject-out cross-validation. The results indicate that our approach provides impressive and robust prediction performance on the EEG dataset without artifact removal process.

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http://hdl.handle.net/10453/132946