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

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

研究成果

15 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルIEEE 20th International Conference on Advanced Communication Technology
ホスト出版物のサブタイトルOpening New Era of Intelligent Things, ICACT 2018
出版社Institute of Electrical and Electronics Engineers Inc.
ページ256-259
ページ数4
2018-February
ISBN(電子版)9791188428007
DOI
出版ステータスPublished - 2018 3 23
イベント20th IEEE International Conference on Advanced Communication Technology, ICACT 2018 - Chuncheon, Korea, Republic of
継続期間: 2018 2 112018 2 14

Other

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

ASJC Scopus subject areas

  • 電子工学および電気工学

フィンガープリント

「EEG-signals based cognitive workload detection of vehicle driver using deep learning」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル