Multi-physiological signal emotion recognition algorithm based on improved Relief F

Abstract

In view of the problem of low accuracy of emotion recognition caused by insufficient feature information of a single physiological signal and the mismatch between individual specificity and the global threshold, an improved Relief F matching multi-physiological signal feature selection algorithm was proposed. Firstly, wavelet packet was used to decompose multiple physiological signals and reconstruct them into six emotional-related bands, and extract 8 types of features based on wavelet coefficients and reconstructed signal IMF components through empirical mode decomposition. Secondly, the Relief F algorithm was used to obtain the preferred feature group first, and then the optimized feature group weight formula was constructed to obtain the global optimal matching feature group and its corresponding matching channel. Finally, the PNN method was used to train the sentiment classification model with the data of the global optimal matching feature set and channel. The results show that the proposed method can classify happiness, anger, relaxation and sadness well. The average recognition accuracy rates are 90.89%, 85.39%, 82.81% and 87.56%, respectively, which is an average increase of 1.76% compared to a single physiological signal. The effectiveness of the proposed method was verified.

Keywords

• multiple physiological signals
• recognition of emotion
• wavelet packet decomposition
• empirical mode decomposition
• probabilistic neural network