Advancing Semi-Supervised EEG Emotion Recognition through Feature Extraction with Mixup and Large Language Models

The scarcity of labeled EEG data presents a significant challenge in emotion recognition. To address this issue, we propose PAWS, a semi-supervised learning framework specifically designed to enhance EEG-based emotion recognition, particularly in scenarios where labeled data is extremely limited. PAWS leverages the power of Intermediate Mixup, which improves domain adaptation by generating more robust features through strategic augmentation. Additionally, PAWS integrates large language models (LLMs) for enhanced feature extraction, enabling the framework to capture complex patterns in EEG signals even with minimal labeled data. In experiments with 1%, 5%, 10%, 15%, and 20% labeled data, PAWS consistently outperformed existing methods, with the most significant performance gains observed in scenarios with very few labeled samples. Notably, with 20% labeled data, PAWS achieved an accuracy of 95.81% ± 0.52% on the SEED dataset and 83.41% ± 0.31% on the SEED-IV dataset. These results demonstrate PAWS’s effectiveness in leveraging limited labeled data to achieve superior emotion recognition performance. This framework not only advances the state-of-the-art in EEG-based emotion recognition but also provides a robust foundation for future research in semi-supervised learning. Code is available at https://github.com/dragonlfy/PAWS.