Regression analysis of EEG signals in fatigue driving based on ensemble learning

Abstract

According to statistics from the World Health Organization, China has always been among the countries with high incidence of traffic accidents, and the main reason is fatigue driving. In recent years, regression analysis of electroencephalogram (EEG) signals has already been a topic of interest within the field of fatigue driving research, yet, it has not been effectively resolved. In this paper, we designed a platform for the collection of EEG signals for fatigue driving that monitors the brain's fatigue state through multiple sensors. Based on the collected EEG data, a framework of fatigue driving regression based on EEG has been proposed. In order to determine the driver's fatigue level, we produced the data set label, which calculates the fatigue index of EEG signals to characterize the level of fatigue. In order to better cope with individual differences, the fatigue index curve was fitted by least squares. At the same time, we proposed an Ensemble Learning driver fatigue index regression analysis method based on the Bayesian model combination, with the support vector regression algorithm as a base learner. By increasing the diversity and difference of the base learners, the performance of the regression analysis method during the process of driver fatigue index regression analysis has been improved. The experimental results showed that the proposed regression analysis method was reliable and could accurately and reliably characterize the driver's fatigue index.