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The research on human emotion under multimedia stimulation based onphysiological signals is an emerging field, and important progress has beenachieved for emotion recognition based on multi-modal signals. However, it ischallenging to make full use of the complementarity amongspatial-spectral-temporal domain features for emotion recognition, as well asmodel the heterogeneity and correlation among multi-modal signals. In thispaper, we propose a novel two-stream heterogeneous graph recurrent neuralnetwork, named HetEmotionNet, fusing multi-modal physiological signals foremotion recognition. Specifically, HetEmotionNet consists of thespatial-temporal stream and the spatial-spectral stream, which can fusespatial-spectral-temporal domain features in a unified framework. Each streamis composed of the graph transformer network for modeling the heterogeneity,the graph convolutional network for modeling the correlation, and the gatedrecurrent unit for capturing the temporal domain or spectral domain dependency.Extensive experiments on two real-world datasets demonstrate that our proposedmodel achieves better performance than state-of-the-art baselines.

HetEmotionNet: Two-Stream Heterogeneous Graph Recurrent Neural Network for Multi-modal Emotion Recognition