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EEG-based driver fatigue detection using hybrid deep generic model.

Phyo Phyo San, Sai Ho Ling, Rifai Chai

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 9, 2017
    PubMed
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    This study introduces a hybrid Deep Generic Model (DGM)-based Support Vector Machine (SVM) for detecting driver fatigue using electroencephalography (EEG). The proposed method effectively enhances feature extraction and classification for improved driver monitoring.

    Area of Science:

    • Biomedical Engineering
    • Machine Learning
    • Transportation Safety

    Background:

    • Driver fatigue is a significant global cause of traffic accidents.
    • Electroencephalography (EEG) based applications are crucial in biomedical engineering for monitoring physiological states.
    • Existing methods for fatigue detection have limitations in feature extraction and classification accuracy.

    Purpose of the Study:

    • To propose a hybrid Deep Generic Model (DGM)-based Support Vector Machine (SVM) for accurate driver fatigue detection using EEG signals.
    • To enhance the integration of unsupervised feature extraction and classification for improved performance.
    • To evaluate the effectiveness of the proposed hybrid model in a real-world driver fatigue monitoring scenario.

    Main Methods:

    • A hybrid model combining a Deep Generic Model (DGM) for unsupervised feature extraction and a Support Vector Machine (SVM) for classification was developed.

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  • The DGM's deep architecture was utilized for learning invariant features from EEG data.
  • SVM was employed to leverage well-behaved features for robust classification, overcoming limitations of individual models.

    • Main Results:

    • The proposed DGM-based SVM model achieved a testing accuracy of 73.29% for driver fatigue detection.
    • The system demonstrated high sensitivity (91.10%) and specificity (55.48%) in identifying fatigue states.
    • The hybrid approach showed improved performance compared to traditional methods by enhancing both feature extraction and classification.

    Conclusions:

    • The hybrid DGM-based SVM is an effective and accurate method for detecting driver fatigue using EEG.
    • This approach offers a promising solution for developing advanced driver fatigue monitoring systems.
    • The study highlights the benefits of integrating deep learning feature extraction with robust classification techniques for biomedical applications.