@inproceedings{9e1ea45446e94d269068fae3e9a14215,
title = "Cognitive Workload Detection from Raw EEG-Signals of Vehicle Driver using Deep Learning",
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.",
keywords = "Cognitive Workload, Deep Learning, Driving, EEG, Neural Networks, Stress",
author = "Almogbel, {Mohammad A.} and Dang, {Anh H.} and Wataru Kameyama",
note = "Publisher Copyright: {\textcopyright} 2019 Global IT Research Institute (GIRI).; 21st International Conference on Advanced Communication Technology, ICACT 2019 ; Conference date: 17-02-2019 Through 20-02-2019",
year = "2019",
month = apr,
day = "29",
doi = "10.23919/ICACT.2019.8702048",
language = "English",
series = "International Conference on Advanced Communication Technology, ICACT",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1167--1172",
booktitle = "21st International Conference on Advanced Communication Technology",
}