Assessment of Mental Fatigue: An EEG-Based Forecasting System for Driving Safety

Liu, YT; Lin, YY; Wu, SL; Hsieh, TY; Lin, CT

Assessment of Mental Fatigue: An EEG-Based Forecasting System for Driving Safety

Liu, YT Lin, YY Wu, SL Hsieh, TY Lin, CT

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015, 2016, pp. 3233 - 3238
Issue Date:: 2016-01-12

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Field	Value	Language
dc.contributor.author	Liu, YT	en_US
dc.contributor.author	Lin, YY	en_US
dc.contributor.author	Wu, SL	en_US
dc.contributor.author	Hsieh, TY	en_US
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.date.issued	2016-01-12	en_US
dc.identifier.citation	Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015, 2016, pp. 3233 - 3238	en_US
dc.identifier.isbn	9781479986965	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119958
dc.description.abstract	© 2015 IEEE. This study proposes an EEG-based forecasting system based on a functional-link recurrent self-evolving fuzzy neural network (FL-RSEFNN) for assessing mental fatigue during a highway driving task. Drivers' cognitive states significantly affect driving safety, especially for fatigue or drowsy driving which is one of common factors to endanger individuals and the public safety. In this study, a FL-RSEFNN employs an on-line gradient descent (GD) learning rule to address the EEG regression problem in brain dynamics for estimation of driving fatigue. We analyze brain dynamics in a car driving task, which is constructed in a simulated virtual reality (VR) environment. The EEG-based forecasting system is evaluated using the generalized cross-subject approach, and the results indicate that the FLRSEFNN is superior to state-of-The-Art models regardless of the use of recurrent or non-recurrent structures.	en_US
dc.relation.ispartof	Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015	en_US
dc.relation.isbasedon	10.1109/SMC.2015.561	en_US
dc.title	Assessment of Mental Fatigue: An EEG-Based Forecasting System for Driving Safety	en_US
dc.type	Conference Proceeding
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
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2015 IEEE. This study proposes an EEG-based forecasting system based on a functional-link recurrent self-evolving fuzzy neural network (FL-RSEFNN) for assessing mental fatigue during a highway driving task. Drivers' cognitive states significantly affect driving safety, especially for fatigue or drowsy driving which is one of common factors to endanger individuals and the public safety. In this study, a FL-RSEFNN employs an on-line gradient descent (GD) learning rule to address the EEG regression problem in brain dynamics for estimation of driving fatigue. We analyze brain dynamics in a car driving task, which is constructed in a simulated virtual reality (VR) environment. The EEG-based forecasting system is evaluated using the generalized cross-subject approach, and the results indicate that the FLRSEFNN is superior to state-of-The-Art models regardless of the use of recurrent or non-recurrent structures.

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http://hdl.handle.net/10453/119958