Using EEG to detect drivers' emotion with Bayesian Networks

Xin An Fan; Lu Zheng Bi; Zhi Long Chen

doi:10.1109/ICMLC.2010.5580919

Using EEG to detect drivers' emotion with Bayesian Networks

Xin An Fan^*, Lu Zheng Bi, Zhi Long Chen

^*Corresponding author for this work

School of Mechanical Engineering

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

25 Citations (Scopus)

Abstract

Driver behavior plays a critical role in driving safety. Besides alcohol and fatigue, emotion is another factor influencing driver behavior. Thus, the detection of driver emotion can contribute to improve driving safety. In this paper, we use Bayesian Network (BNs) to develop a detection model of driver emotion with electroencephalogram (EEG), which considers two factors of driver personality and traffic situation. The preliminary experiment results suggest that this method is feasible and therefore can be used to provide adaptive aiding.

Original language	English
Title of host publication	2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
Pages	1177-1181
Number of pages	5
DOIs	https://doi.org/10.1109/ICMLC.2010.5580919
Publication status	Published - 2010
Event	2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010 - Qingdao, China Duration: 11 Jul 2010 → 14 Jul 2010

Publication series

Name	2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
Volume	3

Conference

Conference	2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
Country/Territory	China
City	Qingdao
Period	11/07/10 → 14/07/10

Keywords

Bayesian networks
Detection model
Driver emotion
Driving safety
EEG

Access to Document

10.1109/ICMLC.2010.5580919

Cite this

@inproceedings{40547073c2984bc9b55218d9f8047d92,

title = "Using EEG to detect drivers' emotion with Bayesian Networks",

abstract = "Driver behavior plays a critical role in driving safety. Besides alcohol and fatigue, emotion is another factor influencing driver behavior. Thus, the detection of driver emotion can contribute to improve driving safety. In this paper, we use Bayesian Network (BNs) to develop a detection model of driver emotion with electroencephalogram (EEG), which considers two factors of driver personality and traffic situation. The preliminary experiment results suggest that this method is feasible and therefore can be used to provide adaptive aiding.",

keywords = "Bayesian networks, Detection model, Driver emotion, Driving safety, EEG",

author = "Fan, {Xin An} and Bi, {Lu Zheng} and Chen, {Zhi Long}",

year = "2010",

doi = "10.1109/ICMLC.2010.5580919",

language = "English",

isbn = "9781424465262",

series = "2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010",

pages = "1177--1181",

booktitle = "2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010",

note = "2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010 ; Conference date: 11-07-2010 Through 14-07-2010",

}

Fan, XA, Bi, LZ & Chen, ZL 2010, Using EEG to detect drivers' emotion with Bayesian Networks. in 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010., 5580919, 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010, vol. 3, pp. 1177-1181, 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010, Qingdao, China, 11/07/10. https://doi.org/10.1109/ICMLC.2010.5580919

Using EEG to detect drivers' emotion with Bayesian Networks. / Fan, Xin An; Bi, Lu Zheng; Chen, Zhi Long.
2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010. 2010. p. 1177-1181 5580919 (2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010; Vol. 3).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Fan, Xin An

AU - Bi, Lu Zheng

AU - Chen, Zhi Long

PY - 2010

Y1 - 2010

N2 - Driver behavior plays a critical role in driving safety. Besides alcohol and fatigue, emotion is another factor influencing driver behavior. Thus, the detection of driver emotion can contribute to improve driving safety. In this paper, we use Bayesian Network (BNs) to develop a detection model of driver emotion with electroencephalogram (EEG), which considers two factors of driver personality and traffic situation. The preliminary experiment results suggest that this method is feasible and therefore can be used to provide adaptive aiding.

AB - Driver behavior plays a critical role in driving safety. Besides alcohol and fatigue, emotion is another factor influencing driver behavior. Thus, the detection of driver emotion can contribute to improve driving safety. In this paper, we use Bayesian Network (BNs) to develop a detection model of driver emotion with electroencephalogram (EEG), which considers two factors of driver personality and traffic situation. The preliminary experiment results suggest that this method is feasible and therefore can be used to provide adaptive aiding.

KW - Bayesian networks

KW - Detection model

KW - Driver emotion

KW - Driving safety

KW - EEG

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U2 - 10.1109/ICMLC.2010.5580919

DO - 10.1109/ICMLC.2010.5580919

M3 - Conference contribution

AN - SCOPUS:78149319487

SN - 9781424465262

T3 - 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010

SP - 1177

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BT - 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010

T2 - 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010

Y2 - 11 July 2010 through 14 July 2010

ER -

Using EEG to detect drivers' emotion with Bayesian Networks

Abstract

Publication series

Conference

Keywords

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