Generalized EEG-based drowsiness prediction system by using a self-organizing neural fuzzy system

Lin, FC; Ko, LW; Chuang, CH; Su, TP; Lin, CT

Generalized EEG-based drowsiness prediction system by using a self-organizing neural fuzzy system

Lin, FC Ko, LW Chuang, CH

Su, TP Lin, CT

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Circuits and Systems I: Regular Papers, 2012, 59 (9), pp. 2044 - 2055
Issue Date:: 2012-09-07

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Field	Value	Language
dc.contributor.author	Lin, FC	en_US
dc.contributor.author	Ko, LW	en_US
dc.contributor.author	Chuang, CH https://orcid.org/0000-0002-5043-8380	en_US
dc.contributor.author	Su, TP	en_US
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.date.issued	2012-09-07	en_US
dc.identifier.citation	IEEE Transactions on Circuits and Systems I: Regular Papers, 2012, 59 (9), pp. 2044 - 2055	en_US
dc.identifier.issn	1549-8328	en_US
dc.identifier.uri	http://hdl.handle.net/10453/115049
dc.description.abstract	A generalized EEG-based Neural Fuzzy system to predict driver's drowsiness was proposed in this study. Driver's drowsy state monitoring system has been implicated as a causal factor for the safety driving issue, especially when the driver fell asleep or distracted in driving. However, the difficulties in developing such a system are lack of significant index for detecting the driver's drowsy state in real-time and the interference of the complicated noise in a realistic and dynamic driving environment. In our past studies, we found that the electroencephalogram (EEG) power spectrum changes were highly correlated with the driver's behavior performance especially the occipital component. Different from presented subject-dependent drowsy state monitor systems, whose system performance may decrease rapidly when different subject applies with the drowsiness detection model constructed by others, in this study, we proposed a generalized EEG-based Self-organizing Neural Fuzzy system to monitor and predict the driver's drowsy state with the occipital area. Two drowsiness prediction models, subject-dependent and generalized cross-subject predictors, were investigated in this study for system performance analysis. Correlation coefficients and root mean square errors are showed as the experimental results and interpreted the performances of the proposed system significantly better than using other traditional Neural Networks (p-value <0.038). Besides, the proposed EEG-based Self-organizing Neural Fuzzy system can be generalized and applied in the subjects' independent sessions. This unique advantage can be widely used in the real-life applications. © 2012 IEEE.	en_US
dc.relation.ispartof	IEEE Transactions on Circuits and Systems I: Regular Papers	en_US
dc.relation.isbasedon	10.1109/TCSI.2012.2185290	en_US
dc.subject.classification	Electrical & Electronic Engineering	en_US
dc.title	Generalized EEG-based drowsiness prediction system by using a self-organizing neural fuzzy system	en_US
dc.type	Journal Article
utslib.citation.volume	9	en_US
utslib.citation.volume	59	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.issue	9	en_US
pubs.publication-status	Published	en_US
pubs.volume	59	en_US

Abstract:

A generalized EEG-based Neural Fuzzy system to predict driver's drowsiness was proposed in this study. Driver's drowsy state monitoring system has been implicated as a causal factor for the safety driving issue, especially when the driver fell asleep or distracted in driving. However, the difficulties in developing such a system are lack of significant index for detecting the driver's drowsy state in real-time and the interference of the complicated noise in a realistic and dynamic driving environment. In our past studies, we found that the electroencephalogram (EEG) power spectrum changes were highly correlated with the driver's behavior performance especially the occipital component. Different from presented subject-dependent drowsy state monitor systems, whose system performance may decrease rapidly when different subject applies with the drowsiness detection model constructed by others, in this study, we proposed a generalized EEG-based Self-organizing Neural Fuzzy system to monitor and predict the driver's drowsy state with the occipital area. Two drowsiness prediction models, subject-dependent and generalized cross-subject predictors, were investigated in this study for system performance analysis. Correlation coefficients and root mean square errors are showed as the experimental results and interpreted the performances of the proposed system significantly better than using other traditional Neural Networks (p-value <0.038). Besides, the proposed EEG-based Self-organizing Neural Fuzzy system can be generalized and applied in the subjects' independent sessions. This unique advantage can be widely used in the real-life applications. © 2012 IEEE.

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http://hdl.handle.net/10453/115049