Workload assessment faces two major issues. That is, how to learn effective fatigue characteristics and how to find the potential state of the workload. This paper proposes a solution to assess the brain fatigue workload of pilots through an instantaneous spectral entropy feature and an infinitely warped model. The instantaneous characteristics of electroencephalography (EEG) signals are extracted by Hilbert transform, and Euclidean norm weighted permutation entropy is proposed. The infinitely warped model is a new automatic learning model for detecting arbitrary shapes of EEG data. In addition, we propose a rapid learning framework to learn mental fatigue by integrating Treelet transform and infinitely warped models. Compared to other state-of-the-art methods, our approach is better able to handle complex data in complex shapes. The experimental results show that this method can more effectively assess the brain fatigue of pilots.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Novel Nonlinear Approach for Real-Time Fatigue EEG Data: An Infinitely Warped Model of Weighted Permutation Entropy


    Contributors:
    Wu, Edmond Q. (author) / Zhu, Li-Min (author) / Zhang, Wen-Ming (author) / Deng, Ping-Yu (author) / Jia, Bo (author) / Chen, Sheng-Di (author) / Ren, He (author) / Zhou, Gui-Rong (author)


    Publication date :

    2020-06-01


    Size :

    7100018 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Time-Scale Independent Permutation Entropy of a Photonic Integrated Device

    Toomey, J. P. / Argyris, A. / McMahon, C. et al. | British Library Online Contents | 2017





    INFINITELY VARIABLE TRANSMISSION

    HALWES DENNIS RAY | European Patent Office | 2019

    Free access