Estimation method of EEG alertness based on multi-feature fusion
Traditional EEG alertness recognition only extracts one type of feature in time domain,frequency domain or nonlinearity,resulting in low accuracy of alertness estimation.Therefore,this paper proposes a multi-feature fusion EEG alertness estimation method.This method first preprocesses the EEG signal,then extracts various features in time domain,frequency domain,and nonlinearity,and further uses the chi-square test for feature selection,and finally inputs the selected features into different classifiers for alertness estimation.The SEED-VIG dataset is used to verify the proposed method.The experimental results show that the EEG alertness estimation method based on multi-feature fusion has a good effect.
EEG signalalertnessmulti-feature fusionchi-square test
万方数据
维普
