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

5 Citations (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

Keywords

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

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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  • 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