EEG-based driving fatigue prediction system using functional-link-based fuzzy neural network

Liu, YT; Lin, YY; Wu, SL; Chuang, CH; Prasad, M; Lin, CT

EEG-based driving fatigue prediction system using functional-link-based fuzzy neural network

Liu, YT Lin, YY Wu, SL Chuang, CH Prasad, M

Lin, CT

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings of the International Joint Conference on Neural Networks, 2014, pp. 4109-4113
Issue Date:: 2014-09-03

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Field	Value	Language
dc.contributor.author	Liu, YT
dc.contributor.author	Lin, YY
dc.contributor.author	Wu, SL
dc.contributor.author	Chuang, CH
dc.contributor.author	Prasad, M https://orcid.org/0000-0002-7745-9667
dc.contributor.author	Lin, CT
dc.date	2014-07-06
dc.date.accessioned	2023-03-24T10:36:19Z
dc.date.available	2023-03-24T10:36:19Z
dc.date.issued	2014-09-03
dc.identifier.citation	Proceedings of the International Joint Conference on Neural Networks, 2014, pp. 4109-4113
dc.identifier.isbn	9781479914845
dc.identifier.issn	2161-4393
dc.identifier.uri	http://hdl.handle.net/10453/168372
dc.description.abstract	This study presents a fuzzy prediction system for the forecasting and estimation of driving fatigue, which utilizes a functional-link-based fuzzy neural network (FLFNN) to predict the drowsiness (DS) level in car driving task. The cognitive state in car driving task is one of key issue in cognitive neuroscience because fatigue driving usually causes enormous losses nowadays. The damage can be extremely decreased by the assistant of various artificial systems. Many Electroencephalography (EEG)-based interfaces have been widely developed recently due to its convenient measurement and real-time response. However, the improvement of recognition accuracy is still confined to some specific problems (e.g., individual difference). In order to solve this issue, the proposed methodology in this paper utilizes a nonlinear fuzzy neural network structure to increase the adaptability in the real-world environment. Therefore, this study is further to analysis the brain activities in car driving, which is constructed in a simulated three-dimensional virtual-reality (VR) environment. Finally, through the development of brain cognitive model in car driving task, this system can predict the cognitive state effectively before drivers' action and then provide correct feedback to users. This study also compared the result with the-state-of-art systems, including Linear Regression (LR), Multi-Layer Perceptron Neural Network (MLPNN) and Support Vector Regression (SVR). Results of this study demonstrate the effectiveness of the proposed FLFNN model.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	Proceedings of the International Joint Conference on Neural Networks
dc.relation.ispartof	International Joint Conference on Neural Networks (IJCNN)
dc.relation.ispartofseries	IEEE International Joint Conference on Neural Networks (IJCNN)
dc.relation.isbasedon	10.1109/IJCNN.2014.6889736
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	EEG-based driving fatigue prediction system using functional-link-based fuzzy neural network
dc.type	Conference Proceeding
utslib.location.activity	Beijing, PEOPLES R CHINA
utslib.for	0902 Automotive Engineering
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/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-24T10:36:17Z
pubs.finish-date	2014-07-11
pubs.publication-status	Published
pubs.start-date	2014-07-06

Abstract:

This study presents a fuzzy prediction system for the forecasting and estimation of driving fatigue, which utilizes a functional-link-based fuzzy neural network (FLFNN) to predict the drowsiness (DS) level in car driving task. The cognitive state in car driving task is one of key issue in cognitive neuroscience because fatigue driving usually causes enormous losses nowadays. The damage can be extremely decreased by the assistant of various artificial systems. Many Electroencephalography (EEG)-based interfaces have been widely developed recently due to its convenient measurement and real-time response. However, the improvement of recognition accuracy is still confined to some specific problems (e.g., individual difference). In order to solve this issue, the proposed methodology in this paper utilizes a nonlinear fuzzy neural network structure to increase the adaptability in the real-world environment. Therefore, this study is further to analysis the brain activities in car driving, which is constructed in a simulated three-dimensional virtual-reality (VR) environment. Finally, through the development of brain cognitive model in car driving task, this system can predict the cognitive state effectively before drivers' action and then provide correct feedback to users. This study also compared the result with the-state-of-art systems, including Linear Regression (LR), Multi-Layer Perceptron Neural Network (MLPNN) and Support Vector Regression (SVR). Results of this study demonstrate the effectiveness of the proposed FLFNN model.

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http://hdl.handle.net/10453/168372