A Study of EEG Correlates of Unexpected Obstacle Dodging Task and Driving Style

In this thesis, we want to study the EEG relates of surprising status and driving style. Accidents usually caused by lack of alertness and awareness have a high fatality rate especially in night driving environments. It becomes extremely dangerous in some situations such as the appearance of an unexpected obstacle in the middle of the road. Combining the technology of virtual reality (VR), a realistic driving environment is developed to provide stimuli to subjects in our research. The VR scene designed in our experiment is driving a car on the freeway at nighttime. Independent Component Analysis (ICA) is used to decompose the sources in the EEG data. ICA combined with power spectrum analysis and correlation analysis is employed to investigate the EEG activity related to surprising level and driving style. According to our experimental results, the appearance of ERP at CPz is highly correlated to the surprising status. Furthermore, the level of surprising status can be evaluated with the amplitude of the ERP. An extension analysis of driving style has also been further studied in the experiments. It is observed that the magnitudes of ERP power spectrum at 10Hz and 20Hz are different respecting to different driving styles.