Cognitive Workload Detection from Raw EEG-Signals of Vehicle Driver using Deep Learning

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

研究成果: Conference contribution

抄録

Electroencephalography (EEG) signals have been proven to be effective in evaluating human's cognitive state under specific tasks. Conventional classification models utilized for EEG classification heavily rely on signal pre-processing and hand-designed features. In this paper, we propose an end-to-end deep neural network which is capable of classifying multiple types of cognitive workload of a vehicle driver and the context of driving using only raw EEG signals as its input without any pre-processing nor the need for conventional hand-designed features. Data used in this study are collected throughout multiple driving sessions conducted on a high-fidelity driving simulator. Experimental results conducted on 4 channels of raw EEG data show that the proposed model is capable of accurately detecting the cognitive workload of a driver and the context of driving.

元の言語English
ホスト出版物のタイトル21st International Conference on Advanced Communication Technology
ホスト出版物のサブタイトルICT for 4th Industrial Revolution!, ICACT 2019 - Proceeding
出版者Institute of Electrical and Electronics Engineers Inc.
ページ1167-1172
ページ数6
ISBN(電子版)9791188428021
DOI
出版物ステータスPublished - 2019 4 29
イベント21st International Conference on Advanced Communication Technology, ICACT 2019 - Pyeongchang, Korea, Republic of
継続期間: 2019 2 172019 2 20

出版物シリーズ

名前International Conference on Advanced Communication Technology, ICACT
2019-February
ISSN(印刷物)1738-9445

Conference

Conference21st International Conference on Advanced Communication Technology, ICACT 2019
Korea, Republic of
Pyeongchang
期間19/2/1719/2/20

Fingerprint

Electroencephalography
Processing
Simulators
Deep learning

Keywords

    ASJC Scopus subject areas

    • Electrical and Electronic Engineering

    これを引用

    Almogbel, M. A., Dang, A. H., & Kameyama, W. (2019). Cognitive Workload Detection from Raw EEG-Signals of Vehicle Driver using Deep Learning. : 21st International Conference on Advanced Communication Technology: ICT for 4th Industrial Revolution!, ICACT 2019 - Proceeding (pp. 1167-1172). [8702048] (International Conference on Advanced Communication Technology, ICACT; 巻数 2019-February). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/ICACT.2019.8702048

    Cognitive Workload Detection from Raw EEG-Signals of Vehicle Driver using Deep Learning. / Almogbel, Mohammad A.; Dang, Anh H.; Kameyama, Wataru.

    21st International Conference on Advanced Communication Technology: ICT for 4th Industrial Revolution!, ICACT 2019 - Proceeding. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1167-1172 8702048 (International Conference on Advanced Communication Technology, ICACT; 巻 2019-February).

    研究成果: Conference contribution

    Almogbel, MA, Dang, AH & Kameyama, W 2019, Cognitive Workload Detection from Raw EEG-Signals of Vehicle Driver using Deep Learning. : 21st International Conference on Advanced Communication Technology: ICT for 4th Industrial Revolution!, ICACT 2019 - Proceeding., 8702048, International Conference on Advanced Communication Technology, ICACT, 巻. 2019-February, Institute of Electrical and Electronics Engineers Inc., pp. 1167-1172, 21st International Conference on Advanced Communication Technology, ICACT 2019, Pyeongchang, Korea, Republic of, 19/2/17. https://doi.org/10.23919/ICACT.2019.8702048
    Almogbel MA, Dang AH, Kameyama W. Cognitive Workload Detection from Raw EEG-Signals of Vehicle Driver using Deep Learning. : 21st International Conference on Advanced Communication Technology: ICT for 4th Industrial Revolution!, ICACT 2019 - Proceeding. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1167-1172. 8702048. (International Conference on Advanced Communication Technology, ICACT). https://doi.org/10.23919/ICACT.2019.8702048
    Almogbel, Mohammad A. ; Dang, Anh H. ; Kameyama, Wataru. / Cognitive Workload Detection from Raw EEG-Signals of Vehicle Driver using Deep Learning. 21st International Conference on Advanced Communication Technology: ICT for 4th Industrial Revolution!, ICACT 2019 - Proceeding. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 1167-1172 (International Conference on Advanced Communication Technology, ICACT).
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