Emotion recognition of EEG signals based on contrastive learning graph convolutional model

Abstract Electroencephalogram (EEG) signals offer invaluable insights into the complexities of emotion generation within the brain. Yet, the variability in EEG signals across individuals presents a formidable obstacle for empirical implementations. Our research addresses these challenges innovatively, focusing on the commonalities within distinct subjects' EEG data.We introduce a novel approach named Contrastive Learning Graph Convolutional Network (CLGCN). This method captures the distinctive features and crucial channel nodes related to individuals' emotional states. Specifically, CLGCN merges the dual benefits of Contrastive Learning's synchronous multisubject data learning and the Graph Convolutional Network's proficiency in deciphering brain connectivity matrices.Understanding multifaceted brain functions and their information interchange processes is realized as CLGCN generates a standardized brain network learning matrix during a dataset's learning process. Our model significantly streamlines the retraining process for new subjects, requiring only 5% of the initial sample size for fine-tuning to attain a remarkable 92.8% accuracy rate. Additionally, our model has undergone extensive testing on the DEAP and SEED datasets, demonstrating the effectiveness of our model.