Correlation study between chaos features of physiological signals and emotions based on the information gain method

Clarifying the correlation between the chaos features of physiological signals and emotions plays an important role in improving the accuracy of emotion recognition. In this paper, the correlations of chaos features such as Lyapunov exponent, complexity, box dimension, and approximate entropy of physiological signals with positive, negative, and neutral emotions are analyzed and studied using the information gain method. Based on this, the Bagging-CHAID model is used for emotion recognition validation analysis. The results show that (1) the approximate entropy of physiological signals has the greatest correlation with positive, negative, and neutral emotions, followed by complexity and Lyapunov exponent features, and box dimensional features have the least correlation with the three emotions, thus, the approximate entropy can be chosen to improve the emotion validated based on Bagging-CHAID model, which showed the accuracy of the correlation analysis.