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As the result of the growing importance of the Human Computer Interfacesystem, understanding human's emotion states has become a consequential abilityfor the computer. This paper aims to improve the performance of emotionrecognition by conducting the complexity analysis of physiological signals.Based on AMIGOS dataset, we extracted several entropy-domain features such asRefined Composite Multi-Scale Entropy (RCMSE), Refined Composite Multi-ScalePermutation Entropy (RCMPE) from ECG and GSR signals, and MultivariateMulti-Scale Entropy (MMSE), Multivariate Multi-Scale Permutation Entropy (MMPE)from EEG, respectively. The statistical results show that RCMSE in GSR has adominating performance in arousal, while RCMPE in GSR would be the excellentfeature in valence. Furthermore, we selected XGBoost model to predict emotionand get 68% accuracy in arousal and 84% in valence.

Entropy-Assisted Multi-Modal Emotion Recognition Framework Based on Physiological Signals