Detecting emergency situations by monitoring drivers' states from EEG

This paper proposes a new method to detect pedestrian sudden occurrence, as an example of emergency situations, by monitoring drivers' state from EEG. Three drivers attended the experiment in a driving simulator with virtual driving environments with EEG signals being collected at twenty standard locations on the scalp. The (LDA) classifier with power spectrum of EEG potentials as input features of the detection model was used to recognize the emergency situation, and (ROC) was used to determine the threshold of the classifier. The experimental results of three healthy subjects indicate that the detection model can recognize the emergency situation within one second (shorter than the response time of drivers) with an accuracy of about 70%, showing that it is feasible to detect emergency situations by monitoring driver's states from EEG.