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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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🏛️國立陽明交通大學機構典藏

Access type: Metadata onlyAuthor: search.filters.author.Jung, TP

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    EEG-based fuzzy neural network estimator for driving performance
    (2004)
    Wu, RC
    ;
    Lin, CT
    ;
    Liang, SF
    ;
    Huang, TY
    ;
    Jung, TP
    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.
  • Thumbnail Image
    Item type:Item,
    Estimating driving performance based on EEG spectrum and fuzzy neural network
    (2004)
    Wu, RC
    ;
    Lin, CT
    ;
    Liang, SF
    ;
    Huang, TY
    ;
    Chen, YC
    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.