Neural Signature and Classification of Emergency Braking Intention Based on Effective Connectivity

In this paper, to further understand the neural mechanism of emergency braking and find new features for emergency braking intention detection, we explore the brain neural correlates of emergency braking intentions by using effective connectivity analysis, which is based on directed transfer function (DTF). Offline analysis shows there exists changes in connectivity between electrodes before and after the onset of emergency. Furthermore, a novel method of emergency braking intention classification was proposed based on connectivity features. Area under curve (AUC) is calculated to evaluate the classification performance of connectivity features. Three preprocessing method were compared. The average AUC of the best method can reach 0.8632, which shows a good classification. This research is of great value for the future development of brain-controlled vehicles.