An Emotional Face Evoked EEG Signal Recognition Method Based on Optimal EEG Feature and Electrodes Selection

Abstract

In this work, we proposed an emotional face evoked EEG signal recognition framework, within this framework the optimal statistic features were extracted from original signals according to time and space, i.e., the span and electrodes. First, the EEG signals were collected using noise suppression methods, and principal component analysis (PCA) was used to reduce dimension and information redundant of data. Then the optimal statistic features were selected and combined from different electrodes based on the classification performance. We also discussed the contribution of each time span of EEG signals in the same electrodes. Finally, experiments using Fisher, Bayes and SVM classifiers show that our methods offer the better chance for reliable classification of the EEG signal. Moreover, the conclusion is supported by physiological evidence as follows: a) the selected electrodes mainly concentrate in temporal cortex of the right hemisphere, which relates with visual according to previous psychological research; b) the selected time span shows that consciousness of the face picture has a trend from posterior brain regions to anterior brain regions.