Driving Fatigue Detecting Based on EEG Signals of Forehead Area

This study examined whether prefrontal brain region electroencephalography (EEG) can be used to detect driver's fatigue. The participants were 13 healthy university students with driving experience. They collected EEG experiments in a virtual driving environment, and divided the collected EEG data into normal state and fatigue state. Fuzzy entropy was used for feature extraction; SVM was used as a classification tool. FP1 and FP2 electrode EEG signal was selected from the subject's EEG signal as analysis object. When single electrode signal was used as feature, accuracy of FP1 was higher than FP2, and if mixing FP1 and FP2 as feature, the accuracy is the highest, the average accuracy is 0.85 by 10-fold cross-validation in Prefrontal brain region. Although the signal classification accuracy of the prefrontal brain region is not the highest, from a practical point, the EEG classification accuracy can be used to detect fatigue.