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

被引：0

作者：

Almogbel, Mohammad A. ^{[1
]}

Dang, Anh H. ^{[2
]}

Kameyama, Wataru ^{[3
]}

机构：

[1] Waseda Univ, Grad Sch Fundamental Sci & Engn, Dept Comp Sci & Commun Engn, Tokyo, Japan

[2] Waseda Univ, GITS, Tokyo, Japan

[3] Waseda Univ, Fac Sci & Engn, Tokyo, Japan

来源：

2018 20TH INTERNATIONAL CONFERENCE ON ADVANCED COMMUNICATION TECHNOLOGY (ICACT) | 2018年

关键词：

Deep Learning; EEG; Neural Networks; Cognitive Workload; Driving; Stress; NEURAL-NETWORK;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：256 / 259

页数：4

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