An Emotion Recognition Method Based On Feature Fusion and Self-Supervised Learning

Emotional diseases being represented in many kinds of human mental and cardiac problems, demanding requirements are imposed on accurate emotion recognition. Deep learning methods have gained widespread application in the field of emotion recognition, utilizing physiological signals. However, many existing methods rely solely on deep features, which can be difficult to interpret and may not provide a comprehensive understanding of physiological signals. To address this issue, we propose a novel emotion recognition method based on feature fusion and self-supervised learning. This approach combines shallow features and deep learning features, resulting in a more holistic and interpretable approach to analyzing physiological signals. In addition, we transferred the self-supervised learning method from processing images to signals, which learns sophisticated and informative features from unlabeled signal data. Our experimental results are conducted on WESAD, a publicly available dataset and the proposed model shows significant improvement in performance, which confirms the superiority of our proposed method compared to state-of-the-art methods.