Behavior Evaluation Based on Electroencephalograph and Personality in a Simulated Driving Experiment - Ding, Changhao; Liu, Mutian; Wang, Yi; Yan, Fuwu; Yan, Lirong (Frontiers in Psychology, 2019)
Assessment and prediction of the driving behavior is very important to improve the traffic safety. We hypothesized that there were some patterns of driving behaviors, and these patterns had some correlation with cognitive states and personalities. To test this hypothesis, an evaluation of driving status based on EEG and steering behavior in a simulated driving experiment was designed and performed. Unity 3D was utilized to design the simulated driving scene. A photoelectric encoder fixed on the steering wheel and the corresponding data collection, transmission and storage device was developed by Arduino to acquire the rotation direction, angle, angular velocity and angular acceleration of the steering wheel. Biopac MP 150 was utilized to collect the EEG data simultaneously during driving. A total of 23 subjects (mean age 23.6±1.3 years, driving years: 2.4±1.6 years, 20 males and 2 females) participated in this study. Fuzzy C-means algorithm (FCMA) was utilized to extract the patterns of driving behavior and cognitive state within the window width of 20s. The behaviors were divided into 5 kinds, i.e. Negative, Normal, Alert, Stress, and Violent behavior respectively, based on the standard deviation of steering wheel data. The cognitive states were divided into 4 kinds, i.e. Negative, Calm, Alert, and Tension respectively, based on the EEG data. The correlation of these data, together with the personality traits evaluated using Cartel 16 Personality Factor Questionnaire (16 PF) were analyzed by using multiclass logistic regression. Results indicated the significance of cognitive state and 7 personality traits (apprehension(O), rule-consciousness(G), reasoning(B), emotional stability(C), liveliness(F), vigilance(L), perfectionism(Q3)) in predicting the driving behaviors and the prediction accuracy was 80.2%. The Negative and Alert cognitive states were highly correlated with the dangerous driving including Negative and Violent behaviors. Personality traits had complicated relationship with driving behaviors, which may vary across different types of subjects and traffic accidents.