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Detection of Driver Vigilance Level Using EEG Signals and Driving Contexts
Guo, Zizheng1,2; Pan, Yufan2; Zhao, Guozhen1; Cao, Shi3; Zhang, Jun2
2018-03-01
Source PublicationIEEE TRANSACTIONS ON RELIABILITY
ISSN0018-9529
SubtypeArticle
Volume67Issue:1Pages:370-380
Contribution Rank1
Abstract

Quantitative estimation of a driver's vigilance level has a great value for improving driving safety and preventing accidents. Previous studies have identified correlations between electroencephalogram (EEG) spectrum power and a driver's mental states such as vigilance and alertness. Studies have also built classification models that can estimate vigilance state changes based on data collected from drivers. In the present study, we propose a system to detect vigilance level using not only a driver's EEG signals but also driving contexts as inputs. We combined a support vector machine with particle swarm optimization methods to improve classification accuracy. A simulated driving task was conducted to demonstrate the reliability of the proposed system. Twenty participants were assigned a 2-h sustained-attention driving task to identify a lead car's brake events. Our system was able to account for 84.1% of experimental reaction times with 162-ms prediction errors. A newly introduced driving context factor, road curves, improved the prediction accuracy by 2-5% with 30-80 ms smaller errors. These findings demonstrated the potential value of the proposed system for estimating driver vigilance level on a time scale of seconds.

KeywordDriver vigilance driving context driving safety electroencephalogram (EEG) support vector machine (SVM)
DOI10.1109/TR.2017.2778754
Indexed BySCI
Language英语
Funding OrganizationNational Key Research and Development Plan(2016YFB1001200) ; National Natural Science Foundation of China(51108390 ; 31771226)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Hardware & Architecture ; Computer Science, Software Engineering ; Engineering, Electrical & Electronic
WOS IDWOS:000426678500030
WOS HeadingsScience & Technology ; Technology
WOS KeywordINDEPENDENT COMPONENT ANALYSIS ; WEARABLE EEG ; SYSTEM ; ALERTNESS ; AWARENESS ; PERFORMANCE ; DROWSINESS ; ARTIFACTS ; WIRELESS ; DYNAMICS
Citation statistics
Cited Times:6[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.psych.ac.cn/handle/311026/26057
Collection中国科学院行为科学重点实验室
Affiliation1.Chinese Acad Sci, Inst Psychol, Key Lab Behav Sci, Beijing 100101, Peoples R China
2.Southwest Jiaotong Univ, Sch Transportat & Logist, Chengdu 610031, Sichuan, Peoples R China
3.Univ Waterloo, Dept Syst Design Engn, Waterloo, ON N2L 3G1, Canada
Recommended Citation
GB/T 7714
Guo, Zizheng,Pan, Yufan,Zhao, Guozhen,et al. Detection of Driver Vigilance Level Using EEG Signals and Driving Contexts[J]. IEEE TRANSACTIONS ON RELIABILITY,2018,67(1):370-380.
APA Guo, Zizheng,Pan, Yufan,Zhao, Guozhen,Cao, Shi,&Zhang, Jun.(2018).Detection of Driver Vigilance Level Using EEG Signals and Driving Contexts.IEEE TRANSACTIONS ON RELIABILITY,67(1),370-380.
MLA Guo, Zizheng,et al."Detection of Driver Vigilance Level Using EEG Signals and Driving Contexts".IEEE TRANSACTIONS ON RELIABILITY 67.1(2018):370-380.
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