Physiological Signals-based Emotion Recognition via High-order Correlation Learning

Pipeline

Abstract

Emotion recognition by physiological signals is an effective way to discern the inner state of human beings and therefore has been widely adopted in many user-centered applications. The majority of current state-of-the-art methods focus on exploring relationship among emotion and physiological signals. Given some particular features of the natural process of emotional expression, it is still a challenging and urgent issue to efficiently combine such high-order correlations among multimodal physiological signals and subjects. To tackle the problem, a novel multi-hypergraph neural networks is proposed, in which one hypergraph is established with one type of physiological signals to formulate inter-subject correlations. Each one of the vertices in a hypergraph stands for one subject with a description of its related stimuli, and the complex correlations among the vertices can be formulated through hyperedges. With the multi-hypergraph structure of the subjects, emotion recognition is translated into classification of vertices in the multi-hypergraph structure. Experimental results with the DEAP dataset and ASCERTAIN dataset demonstrate that the proposed method outperforms the current state-of-the-art methods.

Publication
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM)

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