Analysis of the generalization ability of graph neural networks in cross-subject EEG emotion recognition

Key Points

• Graph neural networks enhance emotion recognition accuracy in cross-subject EEG data, indicating higher generalization ability.
• Performance improved by 15% in novel subject data compared to traditional methods, highlighting the potential of using deep learning.
• Analysis of EEG signals involves sophisticated algorithmic models for better interpretation of emotional states.
• Implications suggest the need for advancing neural network models for more effective recognition in diverse populations.