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

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

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

Abstract

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.

Original languageEnglish
Title of host publication21st International Conference on Advanced Communication Technology
Subtitle of host publicationICT for 4th Industrial Revolution!, ICACT 2019 - Proceeding
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1167-1172
Number of pages6
ISBN (Electronic)9791188428021
DOIs
Publication statusPublished - 2019 Apr 29
Event21st International Conference on Advanced Communication Technology, ICACT 2019 - Pyeongchang, Korea, Republic of
Duration: 2019 Feb 172019 Feb 20

Publication series

NameInternational Conference on Advanced Communication Technology, ICACT
Volume2019-February
ISSN (Print)1738-9445

Conference

Conference21st International Conference on Advanced Communication Technology, ICACT 2019
CountryKorea, Republic of
CityPyeongchang
Period19/2/1719/2/20

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. (2019). Cognitive Workload Detection from Raw EEG-Signals of Vehicle Driver using Deep Learning. In 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; Vol. 2019-February). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/ICACT.2019.8702048