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Volume 8 Issue 7
Jul.  2021

IEEE/CAA Journal of Automatica Sinica

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Ce Zhang and Azim Eskandarian, "A Survey and Tutorial of EEG-Based Brain Monitoring for Driver State Analysis," IEEE/CAA J. Autom. Sinica, vol. 8, no. 7, pp. 1222-1242, July 2021. doi: 10.1109/JAS.2020.1003450
Citation: Ce Zhang and Azim Eskandarian, "A Survey and Tutorial of EEG-Based Brain Monitoring for Driver State Analysis," IEEE/CAA J. Autom. Sinica, vol. 8, no. 7, pp. 1222-1242, July 2021. doi: 10.1109/JAS.2020.1003450

A Survey and Tutorial of EEG-Based Brain Monitoring for Driver State Analysis

doi: 10.1109/JAS.2020.1003450
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  • The driver’s cognitive and physiological states affect his/her ability to control the vehicle. Thus, these driver states are essential to the safety of automobiles. The design of advanced driver assistance systems (ADAS) or autonomous vehicles will depend on their ability to interact effectively with the driver. A deeper understanding of the driver state is, therefore, paramount. Electroencephalography (EEG) is proven to be one of the most effective methods for driver state monitoring and human error detection. This paper discusses EEG-based driver state detection systems and their corresponding analysis algorithms over the last three decades. First, the commonly used EEG system setup for driver state studies is introduced. Then, the EEG signal preprocessing, feature extraction, and classification algorithms for driver state detection are reviewed. Finally, EEG-based driver state monitoring research is reviewed in-depth, and its future development is discussed. It is concluded that the current EEG-based driver state monitoring algorithms are promising for safety applications. However, many improvements are still required in EEG artifact reduction, real-time processing, and between-subject classification accuracy.


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    • Over 100 driver state estimation papers, mostly focused on Brain EEG waves, have been reviewed critically.
    • A comprehensive survey and short tutorial of the most popular signal processing, conventional machine learning classification, and deep learning algorithms for driver state estimation are presented.
    • The future algorithmic requirements of EEG artifact reduction, real-time processing, and between-subject classification accuracy of driver state estimation are discussed.


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