Classification of driving fatigue based on EEG signals

traffic accidents bring serious harm to individuals and society. Fatigue driving has many potential safety hazards, which is the main factor causing road traffic accidents. Therefore, it is urgent to monitor the fatigue system. Firstly, the EEG signals are preprocessed by Butterworth band-pass filter, and then the features are extracted by wavelet transform. The classification results of fatigue EEG signals based on support vector machine are used as the initial fatigue value. Then RANSAC method is used to select fatigue signal. Finally, according to the average value of signals screened by RANSAC method as the standard value, the driver's fatigue state is determined by calculating the Euclidean distance between the standard value and the fatigue EEG signal. The experimental results show that the accuracy of the proposed method is better than that of the traditional method, which can reach 90%. It is easy to use and has wide application value.