Recognizing Hazard Perception in a Visual Blind Area Based on EEG Features

Abstract

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.

Evaluation of sense of security by focusing on frontal EEG beta frequency band on experiencing VR traffic environment

“…Studies have also been conducted to evaluate the driver's awareness of the traffic environment as a vehicle driver using electroencephalography (EEG). Using a driving simulation, Zizheng Guo et al [7] labeled the presence or absence of predictive behavior based on whether the driver accelerates using the accelerator pedal. Using the features calculated from the EEG as input, machine learning with a Support Vector Machine (SVM) was performed, and it was reported that it was possible to discriminate the presence or absence of predictive behavior with an accuracy of 81.06%.…”