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Recognizing Hazard Perception in a Visual Blind Area Based on EEG Features
Guo, Zizheng1,2,3; Pan, Yufan1,4; Zhao, Guozhen5; Zhang, Jun1,2; Dong, Ni1,2,6
2020
Corresponding AuthorGuo, Zizheng(guozizheng@swjtu.edu.cn)
Conference Name不详
Source PublicationIEEE ACCESS
Volume8
Pages48917-48928
Conference Date不详
Conference Place不详
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
AbstractMany potential hazards are encountered during daily driving in mixed traffic situations, and the anticipatory activity of a driver to a hazard is one of the key factors in many crashes. In a previous study using eye-tracking data, it was reliably recognized whether the eyes of a driver had become fixated or pursued hazard cues. A limitation of using eye-tracking data is that it cannot be identified whether the anticipatory activity of a driver to hazards has been activated. This study aimed to propose a method to recognize whether the psychological anticipation of a driver had been activated by a hazard cue using electroencephalogram (EEG) signals as input. Thirty-six drivers participated in a simulated driving task designed according to a standard psychological anticipatory study paradigm. Power spectral density (PSD) features were extracted from raw EEG data, and feature dimensions were reduced by principal component analysis (PCA). The results showed that when a driver detected a hazard cue, the alpha band immediately decreased, and the beta band increased approximately 300 ms after the cue appeared. Based on performance evaluation of the support vector machine (SVM), k-nearest neighbor (KNN) method, and linear discriminant analysis (LDA), SVM could detect the anticipatory activity of the driver to a potential hazard in a timely manner with an accuracy of 81 & x0025;. The findings demonstrated that the hazard anticipatory activity of a driver could be recognized with EEG data as input.
Funding OrganizationNational Natural Science Foundation of China ; Science and Technology Program of Sichuan Province ; Science and Technology Program of China Railway
KeywordHazards Electroencephalography Vehicles Task analysis Psychology Standards Support vector machines Hazard perception EEG anticipatory activity SVM
DOI10.1109/ACCESS.2020.2978436
ISBN2169-3536
Indexed BySCI
Funding ProjectNational Natural Science Foundation of China[51108390] ; National Natural Science Foundation of China[71601163] ; Science and Technology Program of Sichuan Province[2019YFG0043] ; Science and Technology Program of China Railway[2018F024]
Language英语
WOS Research AreaComputer Science ; Engineering ; Telecommunications
WOS SubjectComputer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS IDWOS:000524728000005
Citation statistics
Document Type会议论文
Identifierhttp://ir.psych.ac.cn/handle/311026/31311
Collection中国科学院行为科学重点实验室
Corresponding AuthorGuo, Zizheng
Affiliation1.Southwest Jiaotong Univ, Sch Transportat & Logist, Chengdu 611756, Peoples R China
2.Southwest Jiaotong Univ, Natl United Engn Lab Integrated & Intelligent Tra, Chengdu 611756, Peoples R China
3.Natl Engn Lab Intelligent Transportat Big Data Ap, Chengdu 611756, Peoples R China
4.Southwest Jiaotong Univ, Sch Informat Sci & Technol, Chengdu 611756, Peoples R China
5.Chinese Acad Sci, Inst Psychol, Key Lab Behav Sci, Beijing 100101, Peoples R China
6.Univ Washington, Dept Civil & Environm Engn, Seattle, WA 98195 USA
Recommended Citation
GB/T 7714
Guo, Zizheng,Pan, Yufan,Zhao, Guozhen,et al. Recognizing Hazard Perception in a Visual Blind Area Based on EEG Features[C]:IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC,2020:48917-48928.
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