Assessment of Driver's Driving Performance and Alertness Using EEG-based Fuzzy Neural Networks

Chin Teng Lin; Yu Chieh Chen; Ruei Cheng Wu; Sheng Fu Liang; Teng Yi Huang

doi:10.1109/ISCAS.2005.1464547

Assessment of Driver's Driving Performance and Alertness Using EEG-based Fuzzy Neural Networks

Chin Teng Lin, Yu Chieh Chen, Ruei Cheng Wu, Sheng Fu Liang, Teng Yi Huang

Institute of Medical Informatics

Research output: Contribution to journal › Conference article › peer-review

32 Citations (Scopus)

Abstract

Traffic fatalities in recent years have become a serious concern to our society. Accidents caused by drivers' drowsiness have a high fatality rate due to the decline of drivers' abilities in perception, recognition, and vehicle control abilities while sleepy. Preventing such an accident requires a technique for detecting, estimating, and predicting the level of alertness of a driver and a mechanism to maintain the driver's maximum performance of driving. This paper proposed a system that combines electroencephalogram (EEG) power spectra estimation, independent component analysis and fuzzy neural network models to estimate drivers' cognitive state in a dynamic virtual-reality-based driving environment. Experimental results show that the quantitative driving performance can be accurately and successfully estimated through analyzing driver's EEG signals by the proposed system.

Original language	English
Article number	1464547
Pages (from-to)	152-155
Number of pages	4
Journal	Proceedings - IEEE International Symposium on Circuits and Systems
DOIs	https://doi.org/10.1109/ISCAS.2005.1464547
Publication status	Published - 2005
Event	IEEE International Symposium on Circuits and Systems 2005, ISCAS 2005 - Kobe, Japan Duration: 2005 May 23 → 2005 May 26

All Science Journal Classification (ASJC) codes

Electrical and Electronic Engineering

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10.1109/ISCAS.2005.1464547

Cite this

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title = "Assessment of Driver's Driving Performance and Alertness Using EEG-based Fuzzy Neural Networks",

abstract = "Traffic fatalities in recent years have become a serious concern to our society. Accidents caused by drivers' drowsiness have a high fatality rate due to the decline of drivers' abilities in perception, recognition, and vehicle control abilities while sleepy. Preventing such an accident requires a technique for detecting, estimating, and predicting the level of alertness of a driver and a mechanism to maintain the driver's maximum performance of driving. This paper proposed a system that combines electroencephalogram (EEG) power spectra estimation, independent component analysis and fuzzy neural network models to estimate drivers' cognitive state in a dynamic virtual-reality-based driving environment. Experimental results show that the quantitative driving performance can be accurately and successfully estimated through analyzing driver's EEG signals by the proposed system.",

author = "Lin, {Chin Teng} and Chen, {Yu Chieh} and Wu, {Ruei Cheng} and Liang, {Sheng Fu} and Huang, {Teng Yi}",

year = "2005",

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journal = "Proceedings - IEEE International Symposium on Circuits and Systems",

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note = "IEEE International Symposium on Circuits and Systems 2005, ISCAS 2005 ; Conference date: 23-05-2005 Through 26-05-2005",

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T1 - Assessment of Driver's Driving Performance and Alertness Using EEG-based Fuzzy Neural Networks

AU - Lin, Chin Teng

AU - Chen, Yu Chieh

AU - Wu, Ruei Cheng

AU - Liang, Sheng Fu

AU - Huang, Teng Yi

PY - 2005

Y1 - 2005

N2 - Traffic fatalities in recent years have become a serious concern to our society. Accidents caused by drivers' drowsiness have a high fatality rate due to the decline of drivers' abilities in perception, recognition, and vehicle control abilities while sleepy. Preventing such an accident requires a technique for detecting, estimating, and predicting the level of alertness of a driver and a mechanism to maintain the driver's maximum performance of driving. This paper proposed a system that combines electroencephalogram (EEG) power spectra estimation, independent component analysis and fuzzy neural network models to estimate drivers' cognitive state in a dynamic virtual-reality-based driving environment. Experimental results show that the quantitative driving performance can be accurately and successfully estimated through analyzing driver's EEG signals by the proposed system.

AB - Traffic fatalities in recent years have become a serious concern to our society. Accidents caused by drivers' drowsiness have a high fatality rate due to the decline of drivers' abilities in perception, recognition, and vehicle control abilities while sleepy. Preventing such an accident requires a technique for detecting, estimating, and predicting the level of alertness of a driver and a mechanism to maintain the driver's maximum performance of driving. This paper proposed a system that combines electroencephalogram (EEG) power spectra estimation, independent component analysis and fuzzy neural network models to estimate drivers' cognitive state in a dynamic virtual-reality-based driving environment. Experimental results show that the quantitative driving performance can be accurately and successfully estimated through analyzing driver's EEG signals by the proposed system.

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Assessment of Driver's Driving Performance and Alertness Using EEG-based Fuzzy Neural Networks

Abstract

All Science Journal Classification (ASJC) codes

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