Emotion Recognition from Physiological Signals using Multi-Hypergraph Neural Networks

Abstract

Emotion recognition from physiological signals is an effective way to discern the inner state of users. Existing works are lack in the exploration of latent correlation among multiple physiological signals and relationship among different subjects. To tackle this issue, we propose to recognize emotion from physiological signals using multi-hypergraph neural networks (MHGNN). In this method, the correlation among different subjects is formulated in the multi-hypergraph structure, where each type of physiological signal is used to generate one hypergraph. In each hypergraph, the hyperedges are used to represent the connections among the vertices (subject, stimuli). Thus, the emotion recognition task is modeled as classifying each vertex in the multi-hypergraph. Experimental results and comparisons with the state-of-the-art methods in the DEAP dataset demonstrate the superior performance of our method. The comparative experiments based on available biological knowledge verify that MHGNN can depict the real biological response process in a much more precise way.