In our present work, the sample entropy (SampEn) was used to analyze the changes of ship driver driving fatigue state when they drive for a long time from the perspective of human electroencephalogram (EEG). Combining with the eye movement, the law of fatigue change caused by long-time driving of the ship is analyzed. Thus, it can conclude that the EEG characteristics, as well as the eye movement, can effectively detect driver’s fatigue when they are driving.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Real-time EEG-based detection of ship driving fatigue using sample entropy


    Beteiligte:
    Liu, Yu (Autor:in) / Li, Guohui (Autor:in) / Wang, Fuwang (Autor:in)

    Kongress:

    Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022) ; 2022 ; Wuhan,China


    Erschienen in:

    Proc. SPIE ; 12610


    Erscheinungsdatum :

    28.04.2023





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Study of Steering Wheel Movement under Fatigue Driving and Drunk Driving Based on Sample Entropy

    Li, Z. L. / Jin, X. / Wang, B. J. et al. | British Library Conference Proceedings | 2015


    A Fatigue Driving Detection Algorithm Based on Facial Motion Information Entropy

    Feng You / Yunbo Gong / Haiqing Tu et al. | DOAJ | 2020

    Freier Zugriff

    Accurate Real-time Ship Target detection Using Yolov4

    Wang, Bingde / Han, Bing / Yang, Liutao | IEEE | 2021


    Near Real Time Ship Detection Experiments

    Brusch, S. / Lehner, S. / Schwarz, E. et al. | British Library Conference Proceedings | 2010


    Research on Real-Time Ship Detection Using Deep Learning

    Yu, Jingming / Wang, Jie / Ren, Rong et al. | IEEE | 2022