Exploring the effects of EEG signals on collision cases happening in the process of young drivers’ braking

Abstract

Detecting mental states in drivers offers an opportunity to reduce accidents by triggering alerts and signaling the need for rest or renewed focus. Here we used electroencephalography (EEG) to measure brain signals in young drivers operating a driving simulator to detect mental states and predict accidents. We measured reaction times to unexpected hazardous events and correlated them with EEG signals measured from the frontal, parietal, and temporal cortices as well as the central sulcus (corresponding to motor cortex). We found that EEG signals in the relative beta (power in beta (13–30 Hz) relative to total power of the EEG (0.5–30 Hz)), alpha/delta, alpha/theta, beta/delta, beta/theta frequency bands were higher for collisions than successful collision avoidance, and that the key decision-making period is the 2nd second before braking. Importantly, a decision tree classifier trained on these neural signals predicted collision avoidance outcomes. Then based on random forest model, we extracted three critical neural signals (beta/delta_frontal, relative beta_parietal and relative beta_central Sulcus) to classify collision avoidance outcomes. Our findings suggest measuring EEG during driving may provide useful signals for enhancing driver safety.