Online multi-hypergraph fusion learning for cross-subject emotion recognition

Multimodal fusion for emotion recognition has received increasing attention from researchers because of its ability to effectively leverage multimodal complementary information. However, there are two main challenges lead to performance degradation of existing emotion recognition models, which limits the practical use of existing models. One is that multimodal signals are difficult to fuse effectively to respond to the complexity of emotions. The other is that the individual variability and non-stationarity of physiological signals lead to poor performance of the model on new subjects. In particular, existing methods will not work well when faced with emotion recognition of new subjects in online scenarios. In this paper, we propose a novel online multi-hypergraph fusion learning method (OnMHF) to effectively fuse multimodal information and to reduce the difference between training data and test data for online cross-subject emotion recognition. Specifically, in a training phase, a multi-hypergraph fusion is proposed to fuse multimodal physiological signals to effectively obtain emotion-aware information via leveraging multimodal complementary information and high-order correlations among multimodal signals. In an online recognizing phase, an online multi-hypergraph learning is designed to learn online multimodal information from online multimodal data by updating hypergraph structure. As a result, the proposed method can be more effective for emotion recognition of target subjects when target data arrive in an online manner. Experimental results have demonstrated that the proposed method outperforms the baselines and compared state-of-the-art methods in online emotion recognition tasks.