EEG-based fuzzy neural network estimator for driving performance

Accidents caused by drivers' drowsiness have a high fatality rate because of the marked decline in the drivers' vehicle control abilities. Preventing accidents caused by drowsiness is highly desirable but requires techniques for continuously detecting, estimating, and predicting the level...

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Bibliographic Details
Main Authors: WU, Ruei-Cheng, LIN, Chin-Teng, LIANG, Sheng-Fu, HUANG, Teng-Yi, JUNG, Tzyy-Ping
Format: Conference Proceeding
Language:English
Subjects:
Accidents
Applied sciences
Brain modeling
Computational modeling
Computer science
control theory
systems
Control theory. Systems
Electroencephalography
Exact sciences and technology
Fatigue
Fuzzy neural networks
Safety
Traffic control
Vehicle detection
Vehicle driving
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Summary:Accidents caused by drivers' drowsiness have a high fatality rate because of the marked decline in the drivers' vehicle control abilities. Preventing accidents caused by drowsiness is highly desirable but requires techniques for continuously detecting, estimating, and predicting the level of alertness of drivers. This paper proposes a brain-machine interface that combines electroencephalographic power spectrum estimation, principal component analysis, and fuzzy neural networks to estimate/predict drivers' drowsiness level in a virtual-reality-based driving simulator. The driving performance is defined as deviation between the center of the vehicle and the center of the cruising lane. Our results demonstrated that the proposed method is feasible to accurately estimate quantitatively driving performance in a realistic driving simulator.
ISSN:1062-922X
2577-1655
DOI:10.1109/ICSMC.2004.1400975