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

Research output: Contribution to journalConference article

26 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 languageEnglish
Article number1464547
Pages (from-to)152-155
Number of pages4
JournalProceedings - IEEE International Symposium on Circuits and Systems
DOIs
Publication statusPublished - 2005 Dec 1
EventIEEE International Symposium on Circuits and Systems 2005, ISCAS 2005 - Kobe, Japan
Duration: 2005 May 232005 May 26

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Fuzzy neural networks
Electroencephalography
Accidents
Independent component analysis
Power spectrum
Virtual reality

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Cite this

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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.",
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Assessment of Driver's Driving Performance and Alertness Using EEG-based Fuzzy Neural Networks. / Lin, Chin Teng; Chen, Yu Chieh; Wu, Ruei Cheng; Liang, Sheng-Fu; Huang, Teng Yi.

In: Proceedings - IEEE International Symposium on Circuits and Systems, 01.12.2005, p. 152-155.

Research output: Contribution to journalConference article

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