Brain electrical emotion identification method for extracting features based on empirical wavelet transformation

Abstract

The present invention discloses a brain electrical emotion identification method for extracting features based on the empirical wavelet transformation. The brain electrical emotion identification method comprises the steps of according to the coherence between the brain electrical emotion data peripheral physiological signals and the brain electrical signal channels, selecting the brain electricalchannels having the highest coherence degrees as the feature extraction channels; extracting the brain electrical emotion features based on the empirical wavelet transformation, and obtaining a series of intrinsic mode functions after the empirical wavelet transformation decomposition; calculating the sample entropy of each intrinsic mode function, and constructing the feature vectors based on the sample entropy values to form a feature vector set; dividing the brain electrical emotion data into a plurality of types according to the two dimensions of arousal and titer, and using a support vector machine to identify the brain electrical emotion. The method of the present invention considers the non-linear features and the non-stationary features of the data in a brain electrical emotion data set simultaneously, thereby guaranteeing the classification precision, accuracy and execution speed of the method.

Description

technical field [0001] The invention relates to the field of data mining, in particular to an EEG emotion recognition method for feature extraction based on empirical wavelet transform and sample entropy. Background technique [0002] Emotion is a psychological and physiological process triggered by the perception of the outside world, and it plays an important role in the communication process between people. Emotion recognition has also played an increasingly important role in people's daily life. The methods for recognizing human emotions mainly include facial emotion recognition, voice emotion recognition and EEG emotion recognition, etc., but facial expressions, voice and other features are easy to be detected. A person disguises or disguises. With the development of wearable and dry electrode technology, it is particularly convenient to obtain EEG signals in the real environment for emotion recognition. In the EEG emotion recognition method, it is often difficult to ...

AI Technical Summary

Problems solved by technology

[0004] In summary, the existing feature extraction methods cannot deal with the nonlinear and non-stationary problems of emotional EEG signals well, and the classification accuracy and accuracy are relatively low.

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