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    EEG-based driver fatigue detection using FAWT and multiboosting approaches

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    Author
    Subasi, Abdulhamit cc
    Saikia, Aditya
    Bagedo, Kholoud
    Singh, Amarprit
    Hazarika, Anil
    Subject
    Flexible Analytic Wavelet
    EEG-based driver fatigue detection
    AUC
    F-score
    Subband Frequency Components
    LP channels
    HP channels
    FAWT
    Instance Sickness
    Road Accidents
    Multiboosting Approaches
    Parameters Including Accuracy
    Multiboost Strategy
    Ensemble Methods
    Comprehensive Low Order Features
    Show allShow less
    Date
    2022-10-01
    
    Metadata
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    Abstract
    Globally, 14%–20% of road accidents are mainly due to driver fatigue, the causes of which are instance sickness, travelling for long distance, boredom as a result of driving along the same route consistently, lack of enough sleep, etc. This article presents a flexible analytic wavelet transform (FAWT)-based advanced machine learning method using single modality neurophysiological brain electroencephalogram signals to detect the driver fatigues (i.e., FATIGUE and REST) and to alarm the driver at the earliest to prevent the risks during driving. First, signals of undertaking study groups are subjected to the FAWT that separates the signals into LP and HP channels. Subsequently, relevant subband frequency components with proper setting of tuning parameters are extracted. Then, comprehensive low order features which are statistically significant for p<0.05 , are evaluated from the input subband searched space and embedded them to various ensemble methods under multiboost strategy. Results are evaluated in terms of various parameters including accuracy, F-score, AUC, and κ . Results show that the proposed approach is promising in classification and it achieves optimum individual accuracies of 97.10% and 97.90% in categorizing FATIGUE and REST states with F-score of 97.50%, AUC of 0.975, and κ of 0.950. Comparison of the proposed method with the prior methods in the context of feature, accuracy, and modality profiles undertaken, indicates the effectiveness and reliability of the proposed method for real-world applications.
    Department
    Computer Science
    Publisher
    IEEE
    Journal title
    IEEE Transactions on Industrial Informatics
    DOI
    doi: 10.1109/TII.2022.3167470
    ae974a485f413a2113503eed53cd6c53
    doi: 10.1109/TII.2022.3167470
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