Recognizing hazard perception in a visual blind area based on EEG features

被引：0
|
作者：
Guo, Zizheng [1 ,2 ,3 ]
Pan, Yufan [1 ,4 ]
Zhao, Guozhen [5 ]
Zhang, Jun [1 ,2 ]
Dong, Ni [1 ,2 ,6 ]
机构：
[1] School of Transportation and Logistics, Southwest Jiaotong University, Chengdu,611756, China
[2] National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University, Chengdu,611756, China
[3] National Engineering Laboratory of Intelligent Transportation Big Data Application Technology, Chengdu,611756, China
[4] School of Information Science and Technology, Southwest Jiaotong University, Chengdu,611756, China
[5] Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing,100101, China
[6] Department of Civil and Environmental Engineering, University of Washington, Seattle,WA,98195, United States
来源：
IEEE Access | 2020年 / 8卷
关键词：
Many 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%. The findings demonstrated that the hazard anticipatory activity of a driver could be recognized with EEG data as input. © 2013 IEEE;
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页码：48917 / 48928
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