用于跨数据集基于脑电图的情绪识别的掩码生成对比表示学习
Masked Generative-Contrastive Representation Learning for Cross-Dataset EEG-Based Emotion Recognition
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中文总结 AI 辅助
提出掩码生成对比表示学习框架MGCRL用于跨数据集脑电图情绪识别，基于区域感知时空编码器，整合生成与对比学习，通过关键设计实现跨数据集泛化的细粒度和全局判别表示。
中文摘要 AI 辅助
自监督学习（SSL）在跨数据集迁移方面潜力巨大，但在基于脑电图的情绪识别中应用较少。现有SSL方法难以应对脑电图信号复杂时空依赖等问题。为此提出MGCRL，基于区域感知时空编码器，集成生成和对比学习，通过三项关键设计实现跨数据集泛化的细粒度和全局判别表示，实验验证了其有效性。
英文摘要
Self-supervised learning (SSL) shows strong potential for cross-dataset transfer by improving feature representation and generalization. However, its application to EEG-based emotion recognition remains largely unexplored. Existing SSL methods struggle to capture the intricate spatiotemporal dependencies of EEG signals under varying channel configurations, extract fine-grained representations resilient to noise, and derive global features that generalize well across subjects. To address these challenges, we propose Masked Generative-Contrastive Representation Learning (MGCRL), a novel SSL framework specifically designed for EEG-based emotion recognition. Built upon a region-aware spatiotemporal encoder, MGCRL integrates generative and contrastive learning to achieve both fine-grained and global discriminative representations for cross-dataset generalization. MGCRL introduces three key designs: 1) a spatiotemporal encoder that incorporates region-based graph convolution to capture localized spatial and functional relationships, enhancing region-specific feature learning and mitigating the impact of varying EEG channel configurations across datasets; 2) a generative learning mechanism based on the joint embedding predictive architecture (JEPA) that utilizes masked features to capture noise robustness fine-grained representations, improving the model's capability to characterize subtle emotional states; and 3) a contrastive learning strategy that leverages masked and original features to learn temporally stable and cross-subject-invariant representations across the same stimuli, boosting emotion discrimination and cross-subject generalization. Under these designs, MGCRL exhibits remarkable ability to learn universal representation. Extensive experiments involving pretraining on the large FACED dataset and fine-tuning on multiple SEED-series datasets demonstrate the effectiveness of MGCRL.
