用流形对比学习建模跨人情绪连续性，提升脑电情感识别准确率。
MGMCL: Multi-Granularity Manifold Contrastive Learning With Neural ODEs for Cross-Subject EEG Emotion Recognition
- 在流形空间中分层次进行对比学习，捕捉情绪的连续动态变化
- 在三个公开数据集上分别达到91.23%、73.82%、76.38%准确率
- 适合做跨被试情绪识别与连续情感建模的研究者参考
跨被试脑电（EEG）情感识别因个体差异大且传统方法忽略情感连续性而面临挑战。现有方法多在欧氏空间操作，仅对齐边缘分布，未能保留情绪语义结构。本文提出MGMCL，将情感识别重构为在对称正定（SPD）黎曼流形上的连续表示学习。框架在实例、情绪和轨迹三个粒度上引入流形对比学习，并保持语义顺序。通过流形上的神经常微分方程建模连续情感动态，利用格罗莫夫-瓦瑟斯坦流形对齐实现跨被试泛化。弱监督学习支持从离散标签预测连续效价-唤醒-主导维度。在三个公开数据集上实验表明，性能达91.23%（SEED）、73.82%（SEED-IV）、76.38%（DEAP），分别优于此前最佳方法1.89%、1.66%、1.28%。
原文摘要 · Abstract (English)
Cross-subject electroencephalogram (EEG)-based emotion recognition remains challenging due to substantial inter-individual variability and discrete formulation that overlooks affective continuity. Existing methods operate in Euclidean space and focus on marginal distribution alignment, failing to preserve the semantic structure of emotions across subjects. This article proposes MGMCL, reconceptualizing emotion recognition as learning continuous representations on symmetric positive definite (SPD) Riemannian manifolds. The frame?work introduces multi-granularity manifold contrastive learning at instance, emotion, and trajectory levels while preserving semantic ordering. Neural ordinary differential equations on manifolds model continuous emotion dynamics. Cross-subject generalization employs Gromov-Wasserstein manifold alignment. Weakly-supervised learning enables continuous valence-arousal-dominance prediction from discrete labels. Extensive experiments on three public datasets demonstrate state-of-the-art performance: 91.23% accuracy on SEED, 73.82% on SEED-IV, and 76.38% on DEAP, achieving consistent improvements of 1.89%, 1.66%, and 1.28% over previous best methods, respectively.
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