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

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

doi:10.23919/ICACT.2018.8323716

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 proceeding › Conference contribution

24 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 language	English
Title of host publication	IEEE 20th International Conference on Advanced Communication Technology
Subtitle of host publication	Opening New Era of Intelligent Things, ICACT 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	256-259
Number of pages	4
Volume	2018-February
ISBN (Electronic)	9791188428007
DOIs	https://doi.org/10.23919/ICACT.2018.8323716
Publication status	Published - 2018 Mar 23
Event	20th IEEE International Conference on Advanced Communication Technology, ICACT 2018 - Chuncheon, Korea, Republic of Duration: 2018 Feb 11 → 2018 Feb 14

Other

Other	20th IEEE International Conference on Advanced Communication Technology, ICACT 2018
Country/Territory	Korea, Republic of
City	Chuncheon
Period	18/2/11 → 18/2/14

Keywords

Cognitive Workload
Deep Learning
Driving
EEG
Neural Networks
Stress

ASJC Scopus subject areas

Electrical and Electronic Engineering

Access to Document

10.23919/ICACT.2018.8323716

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 proceeding › Conference 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 doi: 10.23919/ICACT.2018.8323716

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

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Keywords

ASJC Scopus subject areas

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