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
SN - 1098-7576
VL - 1
SP - 585
EP - 590
JO - IEEE International Conference on Neural Networks - Conference Proceedings
JF - IEEE International Conference on Neural Networks - Conference Proceedings
T2 - 2004 IEEE International Joint Conference on Neural Networks - Proceedings
Y2 - 25 July 2004 through 29 July 2004
ER -