Estimating Driver Performance Using Multiple Electroencephalography (EEG)-Based Regression Algorithms.

摘要

Poor driving caused by fatigue and drowsiness is the cause of many car accidents each year despite a number of vehicle-mounted sensors designed to infer driver state from behavior. Recent research into the neural correlates of fatigue has suggested the potential for the use of electroencephalographic (EEG) signals to not only detect the fatigue onset in drivers, but also predict the corresponding drop in performance. Here, we sought to compare the performance of 3 EEG regression approaches (linear principal component PC), linear support vector regression (SVR), and radial basis function SVR designed to provide continuous estimates of driver error during an extended simulated driving session. Eleven subjects were asked to maintain the heading and speed of their vehicle during 45 min of simulated driving, with average deviation from the center of the cruising lane used as the measure of driver error. Lane deviation and 64-channel EEG data were recorded during the session and processed offline, and 10-fold cross validation was used to assess model performance. In general, all 3 approaches produced significantly correlated estimates of driver error; however, the correlation coefficients varied significantly between cross-validation blocks, potentially because of inter- block variability in the measure of driver error. Prediction errors of both SVR- based models were significantly smaller than those of the PC-based model, but no difference was found between SVR-based approaches. These results indicate that regression models can be used to extract continuous information of driver performance; however, the variability in correlation analysis suggests that lane deviation may not be an ideal measure of driver performance, and that regression performance may improve from a more stable metric of driver performance as simulated driving complexity increases.