Fine-Grained EEG Emotion Recognition Using Lite Residual Convolution-Based Transformer Neural Network

Current electroencephalogram (EEG)-based emotion recognition research confronts two main challenges. On the one hand, most EEG emotion datasets have limited stimulus diversity and coarse-grained emotional labeling. On the other hand, there is a contradiction between increasingly complex network models and the limited resources of EEG training data. To address these issues, this paper builds a fine-grained EEG emotion dataset for Chinese storytelling (FEEDC) and proposes the lite residual convolution-based transformer neural network (Lite-Resformer), which deeply explores feature information in the time-frequency-space domain of EEG data to improve emotion recognition accuracy, especially positive emotion. First, a fine-grained EEG emotion dataset is constructed using videos from mainstream Chinese social media platforms, covering emotional stimuli related to new productive forces, natural landscapes, and traditional culture. Valid EEG signals and subjective self-assessment data from 65 participants are recorded. Next, a 3D brain map feature superposition method is used to effectively fuse and characterize the frequency, space, and time information of EEG signals. Despite the small amount of EEG data, its rich emotional information motivates a lightweight residual network and Transformer-based network named the Lite-Resformer model, which effectively combines electrode location, spatial, and temporal information from EEG signals, extracting both local and global features. The proposed model has been validated for its effectiveness in valence-arousal emotion classification tasks and achieves an accuracy of 85.48% in the eight-class classification task for discrete emotions, outperforming the existing state-of-the-art methods. This paper provides an effective solution for lightweight finegrained EEG emotion recognition and is of significant value for exploring the emotional factors influencing the spread of Chinese stories