Zi Ang Du Ye Xia ^* Pujie Zhao ^*

• Xi'an Research Institute of High Technology, Xi’an, China

Physiological signal recognition is crucial in emotion recognition, and recent advancements in multi-modal fusion have enabled the integration of various physiological signals for improved recognition tasks. However, current models for emotion recognition with hyper complex multimodal signals face limitations due to fusion methods and insufficient attention mechanisms, preventing further enhancement in classification performance.To address these challenges, we propose a new model framework named Signal Channel Attention Network(SCA-Net), which comprises three main components: an encoder, an attention fusion module, and a decoder. In the attention fusion module, we developed five types of attention mechanisms inspired by existing research and performed comparative experiments using the public dataset MAHNOB-HCI. These experiments demonstrated some improvements in effectiveness.We also conducted ablation experiments within the most effective attention fusion module to verify the benefits of multi-modal fusion. Additionally, we adjusted the training process for different attention fusion modules by employing varying early stopping parameters to prevent model overfitting.