Estimating driving performance based on EEG spectrum and fuzzy neural network

Ruei Cheng Wu, Chin Teng Lin, Sheng Fu Liang, Te Yi Huang, Yu Chieh Chen, Tzyy Ping Jung

Research output: Contribution to journalConference article

11 Citations (Scopus)

Abstract

The growing number of traffic fatalities in recent years has become a serious concern to society. Accidents caused by drivers' drowsiness behind the steering wheel have a high fatality rate because of the marked decline in the drivers' abilities of perception, recognition, and vehicle control abilities while sleepy. Preventing accidents caused by drowsiness requires a technique for detecting, estimating, and predicting the level of alertness of a driver and a mechanism for maintaining his/her maximum performance. This paper describes a system that combines electroencephalographic (EEG) power spectrum estimation, principal component analysis, and fuzzy neural network model to estimate/predict drivers' drowsiness level in a driving simulator. Our results demonstrated that, for the first time, it is feasible to accurately estimate task performance, accurately estimate quantitatively measured driving performance, expressed as deviation between the center of the vehicle and the center of the cruising lane, in a realistic driving simulation.

Original languageEnglish
Pages (from-to)585-590
Number of pages6
JournalIEEE International Conference on Neural Networks - Conference Proceedings
Volume1
Publication statusPublished - 2004 Dec 1
Event2004 IEEE International Joint Conference on Neural Networks - Proceedings - Budapest, Hungary
Duration: 2004 Jul 252004 Jul 29

Fingerprint

Fuzzy neural networks
Accidents
Power spectrum
Principal component analysis
Wheels
Simulators

All Science Journal Classification (ASJC) codes

  • Software

Cite this

Wu, Ruei Cheng ; Lin, Chin Teng ; Liang, Sheng Fu ; Huang, Te Yi ; Chen, Yu Chieh ; Jung, Tzyy Ping. / Estimating driving performance based on EEG spectrum and fuzzy neural network. In: IEEE International Conference on Neural Networks - Conference Proceedings. 2004 ; Vol. 1. pp. 585-590.
@article{ab5729e0f203435a90fa77871be5d276,
title = "Estimating driving performance based on EEG spectrum and fuzzy neural network",
abstract = "The growing number of traffic fatalities in recent years has become a serious concern to society. Accidents caused by drivers' drowsiness behind the steering wheel have a high fatality rate because of the marked decline in the drivers' abilities of perception, recognition, and vehicle control abilities while sleepy. Preventing accidents caused by drowsiness requires a technique for detecting, estimating, and predicting the level of alertness of a driver and a mechanism for maintaining his/her maximum performance. This paper describes a system that combines electroencephalographic (EEG) power spectrum estimation, principal component analysis, and fuzzy neural network model to estimate/predict drivers' drowsiness level in a driving simulator. Our results demonstrated that, for the first time, it is feasible to accurately estimate task performance, accurately estimate quantitatively measured driving performance, expressed as deviation between the center of the vehicle and the center of the cruising lane, in a realistic driving simulation.",
author = "Wu, {Ruei Cheng} and Lin, {Chin Teng} and Liang, {Sheng Fu} and Huang, {Te Yi} and Chen, {Yu Chieh} and Jung, {Tzyy Ping}",
year = "2004",
month = "12",
day = "1",
language = "English",
volume = "1",
pages = "585--590",
journal = "IEEE International Conference on Neural Networks - Conference Proceedings",
issn = "1098-7576",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

Estimating driving performance based on EEG spectrum and fuzzy neural network. / Wu, Ruei Cheng; Lin, Chin Teng; Liang, Sheng Fu; Huang, Te Yi; Chen, Yu Chieh; Jung, Tzyy Ping.

In: IEEE International Conference on Neural Networks - Conference Proceedings, Vol. 1, 01.12.2004, p. 585-590.

Research output: Contribution to journalConference article

TY - JOUR

T1 - Estimating driving performance based on EEG spectrum and fuzzy neural network

AU - Wu, Ruei Cheng

AU - Lin, Chin Teng

AU - Liang, Sheng Fu

AU - Huang, Te Yi

AU - Chen, Yu Chieh

AU - Jung, Tzyy Ping

PY - 2004/12/1

Y1 - 2004/12/1

N2 - The growing number of traffic fatalities in recent years has become a serious concern to society. Accidents caused by drivers' drowsiness behind the steering wheel have a high fatality rate because of the marked decline in the drivers' abilities of perception, recognition, and vehicle control abilities while sleepy. Preventing accidents caused by drowsiness requires a technique for detecting, estimating, and predicting the level of alertness of a driver and a mechanism for maintaining his/her maximum performance. This paper describes a system that combines electroencephalographic (EEG) power spectrum estimation, principal component analysis, and fuzzy neural network model to estimate/predict drivers' drowsiness level in a driving simulator. Our results demonstrated that, for the first time, it is feasible to accurately estimate task performance, accurately estimate quantitatively measured driving performance, expressed as deviation between the center of the vehicle and the center of the cruising lane, in a realistic driving simulation.

AB - The growing number of traffic fatalities in recent years has become a serious concern to society. Accidents caused by drivers' drowsiness behind the steering wheel have a high fatality rate because of the marked decline in the drivers' abilities of perception, recognition, and vehicle control abilities while sleepy. Preventing accidents caused by drowsiness requires a technique for detecting, estimating, and predicting the level of alertness of a driver and a mechanism for maintaining his/her maximum performance. This paper describes a system that combines electroencephalographic (EEG) power spectrum estimation, principal component analysis, and fuzzy neural network model to estimate/predict drivers' drowsiness level in a driving simulator. Our results demonstrated that, for the first time, it is feasible to accurately estimate task performance, accurately estimate quantitatively measured driving performance, expressed as deviation between the center of the vehicle and the center of the cruising lane, in a realistic driving simulation.

UR - http://www.scopus.com/inward/record.url?scp=10944248819&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=10944248819&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:10944248819

VL - 1

SP - 585

EP - 590

JO - IEEE International Conference on Neural Networks - Conference Proceedings

JF - IEEE International Conference on Neural Networks - Conference Proceedings

SN - 1098-7576

ER -