This research addresses the critical issue of fear detection in pilots within the aviation domain, recognizing its profound impact on performance and flight safety. By leveraging Electroencephalogram (EEG) data and employing a Decision Tree algorithm, this study aims to discern varying levels of fear experienced by pilots, particularly during the crucial landing phase. EEG signals were collected during simulated flight operations, categorized into five fear levels, ranging from none to extreme. Evaluation of the model's performance, utilizing a confusion matrix, revealed an impressive average accuracy of 92.95%. The potential application of this system as a tool for identifying fear in aspiring pilots holds promise for enhancing overall flight safety standards.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Pilot Fear Detection from EEG Signals Classified by Decision Tree During Landing Conditions


    Beteiligte:


    Erscheinungsdatum :

    2024-02-21


    Format / Umfang :

    881389 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    New C-fuzzy decision tree with classified points

    Yang, S.-B. | British Library Online Contents | 2008


    LANDING DECISION ASSISTANCE SYSTEM, LANDING DECISION ASSISTANCE METHOD, AND LANDING DECISION ASSISTANCE PROGRAM

    IIJIMA TOMOKO / MATAYOSHI NAOKI / YOSHIKAWA EIICHI | Europäisches Patentamt | 2017

    Freier Zugriff

    Analysis of Pilot Control Behavior During Balked Landing Maneuvers

    Hoermann, Hans / Van Den Berg, Pim / Peixoto, Julio et al. | AIAA | 2005



    Prediction of Pilot Performance During Initial Carrier Landing Qualification

    C. A. Brictson / W. J. Burger / T. Gallagher | NTIS | 1972