Temporal EEG imaging for drowsy driving prediction

Cheng, EJ; Young, KY; Lin, CT

Temporal EEG imaging for drowsy driving prediction

Cheng, EJ Young, KY Lin, CT

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Publication Type:: Journal Article
Citation:: Applied Sciences (Switzerland), 2019, 9 (23)
Issue Date:: 2019-12-01

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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.accessioned	2020-04-01T04:06:04Z
dc.date.available	2020-04-01T04:06:04Z
dc.date.issued	2019-12-01	en_US
dc.identifier.citation	Applied Sciences (Switzerland), 2019, 9 (23)	en_US
dc.identifier.uri	http://hdl.handle.net/10453/139700
dc.description.abstract	© 2019 by the authors. As a major cause of vehicle accidents, the prevention of drowsy driving has received increasing public attention. Precisely identifying the drowsy state of drivers is difficult since it is an ambiguous event that does not occur at a single point in time. In this paper, we use an electroencephalography (EEG) image-based method to estimate the drowsiness state of drivers. The driver's EEG measurement is transformed into an RGB image that contains the spatial knowledge of the EEG. Moreover, for considering the temporal behavior of the data, we generate these images using the EEG data over a sequence of time points. The generated EEG images are passed into a convolutional neural network (CNN) to perform the prediction task. In the experiment, the proposed method is compared with an EEG image generated from a single data time point, and the results indicate that the approach of combining EEG images in multiple time points is able to improve the performance for drowsiness prediction.	en_US
dc.relation.ispartof	Applied Sciences (Switzerland)	en_US
dc.relation.isbasedon	10.3390/app9235078	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Temporal EEG imaging for drowsy driving prediction	en_US
dc.type	Journal Article
utslib.citation.volume	23	en_US
utslib.citation.volume	9	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	open_access	*
pubs.issue	23	en_US
pubs.publication-status	Published	en_US
pubs.volume	9	en_US

Abstract:

© 2019 by the authors. As a major cause of vehicle accidents, the prevention of drowsy driving has received increasing public attention. Precisely identifying the drowsy state of drivers is difficult since it is an ambiguous event that does not occur at a single point in time. In this paper, we use an electroencephalography (EEG) image-based method to estimate the drowsiness state of drivers. The driver's EEG measurement is transformed into an RGB image that contains the spatial knowledge of the EEG. Moreover, for considering the temporal behavior of the data, we generate these images using the EEG data over a sequence of time points. The generated EEG images are passed into a convolutional neural network (CNN) to perform the prediction task. In the experiment, the proposed method is compared with an EEG image generated from a single data time point, and the results indicate that the approach of combining EEG images in multiple time points is able to improve the performance for drowsiness prediction.

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http://hdl.handle.net/10453/139700