Grop: Graph Orthogonal Purification Network for EEG Emotion Recognition

The existence of emotion-irrelevant representations and individual variability impedes the extraction of robust emotional representations, limiting the adaptability of EEG emotion recognition. Massive studies focus on the mining of emotion-aware information, overlooking emotion-agnostic information, which is insufficient for the extraction of emotion-relevant features against redundancy and variation. In this paper, Graph Orthogonal Purification Network (Grop) is proposed to enhance individual adaptability through improvements in the orthogonality and transferability between emotion-relevant and emotion-irrelevant features. Specifically, the proposed Grop utilized a graph representation extraction module to capture both emotion-relevant and emotion-irrelevant features by the dual graph. The representation orthogonal purification module is developed to eliminate redundant information through feature projection and feature purification. Moreover, the dual emotional space alignment module is imposed to align distribution discrepancies in different emotion feature spaces. To assess the effectiveness of the proposed Grop, various experiments are conducted on two public EEG emotion datasets, i.e., SEED and SEED-IV. The results achieve state-of-the-art performance, demonstrating the capability of the Grop to capture robust emotion features and alleviate the intra- and inter-subject discrepancies.