EEG-signals based cognitive workload detection of vehicle driver using deep learning

Mohammad A. Almogbel, Anh H. Dang, Wataru Kameyama

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1 Citation (Scopus)

    Abstract

    Vehicle driver's ability to maintain optimal performance and attention is essential to ensure the safety of the traffic. Electroencephalography (EEG) signals have been proven to be effective in evaluating human's cognitive state under specific tasks. In this paper, we propose the use of deep learning on EEG signals to detect the driver's cognitive workload under high and low workload tasks. Data used in this research are collected throughout multiple driving sessions conducted on a high fidelity driving simulator. Preliminary experimental results conducted on only 4 channels of EEG show that the proposed system is capable of accurately detecting the cognitive workload of the driver with an enormous potential for improvement.

    Original languageEnglish
    Title of host publicationIEEE 20th International Conference on Advanced Communication Technology
    Subtitle of host publicationOpening New Era of Intelligent Things, ICACT 2018
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages256-259
    Number of pages4
    Volume2018-February
    ISBN (Electronic)9791188428007
    DOIs
    Publication statusPublished - 2018 Mar 23
    Event20th IEEE International Conference on Advanced Communication Technology, ICACT 2018 - Chuncheon, Korea, Republic of
    Duration: 2018 Feb 112018 Feb 14

    Other

    Other20th IEEE International Conference on Advanced Communication Technology, ICACT 2018
    CountryKorea, Republic of
    CityChuncheon
    Period18/2/1118/2/14

    Fingerprint

    Electroencephalography
    Simulators
    Deep learning

    Keywords

    • Cognitive Workload
    • Deep Learning
    • Driving
    • EEG
    • Neural Networks
    • Stress

    ASJC Scopus subject areas

    • Electrical and Electronic Engineering

    Cite this

    Almogbel, M. A., Dang, A. H., & Kameyama, W. (2018). EEG-signals based cognitive workload detection of vehicle driver using deep learning. In IEEE 20th International Conference on Advanced Communication Technology: Opening New Era of Intelligent Things, ICACT 2018 (Vol. 2018-February, pp. 256-259). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/ICACT.2018.8323716

    EEG-signals based cognitive workload detection of vehicle driver using deep learning. / Almogbel, Mohammad A.; Dang, Anh H.; Kameyama, Wataru.

    IEEE 20th International Conference on Advanced Communication Technology: Opening New Era of Intelligent Things, ICACT 2018. Vol. 2018-February Institute of Electrical and Electronics Engineers Inc., 2018. p. 256-259.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Almogbel, MA, Dang, AH & Kameyama, W 2018, EEG-signals based cognitive workload detection of vehicle driver using deep learning. in IEEE 20th International Conference on Advanced Communication Technology: Opening New Era of Intelligent Things, ICACT 2018. vol. 2018-February, Institute of Electrical and Electronics Engineers Inc., pp. 256-259, 20th IEEE International Conference on Advanced Communication Technology, ICACT 2018, Chuncheon, Korea, Republic of, 18/2/11. https://doi.org/10.23919/ICACT.2018.8323716
    Almogbel MA, Dang AH, Kameyama W. EEG-signals based cognitive workload detection of vehicle driver using deep learning. In IEEE 20th International Conference on Advanced Communication Technology: Opening New Era of Intelligent Things, ICACT 2018. Vol. 2018-February. Institute of Electrical and Electronics Engineers Inc. 2018. p. 256-259 https://doi.org/10.23919/ICACT.2018.8323716
    Almogbel, Mohammad A. ; Dang, Anh H. ; Kameyama, Wataru. / EEG-signals based cognitive workload detection of vehicle driver using deep learning. IEEE 20th International Conference on Advanced Communication Technology: Opening New Era of Intelligent Things, ICACT 2018. Vol. 2018-February Institute of Electrical and Electronics Engineers Inc., 2018. pp. 256-259
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