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Article Synopsis
- EEG-based emotion recognition analyzes neural activity to predict emotions but faces challenges due to the variability of data across subjects in time, frequency, and spatial domains.
- To tackle these issues, a new model called Temporal-Spectral Graph Convolutional Network (TSGCN) is introduced, which uses a Minimum Category Confusion loss to align subjective and predefined emotional labels.
- Experiments show that TSGCN outperforms existing methods in emotion recognition and exhibits high robustness, largely due to its unique approach and the integration of Deep and Shallow feature Dynamic Adversarial Learning for cross-subject adaptation.