将脑电数据视为黎曼流形上的动态，提升模型泛化能力
ManifoldFormer: Geometric Deep Learning for Neural Dynamics on Riemannian Manifolds
- 用黎曼变分自编码器学习脑电信号的几何结构
- 在流形上构建测地线感知的注意力机制，精度提升4.6%-4.8%
- 适合研究脑机接口与神经动力学建模的学者
现有脑电基础模型多将神经信号视为欧氏空间中的通用时间序列，忽略了神经动力学内在的几何结构——其活动受限于低维流形。这种模型假设与神经几何间的根本性错配，限制了表征质量与跨被试泛化能力。ManifoldFormer提出一种新型几何深度学习框架，显式学习神经流形表示：集成三个核心创新——用于流形嵌入的黎曼变分自编码器、在神经流形上直接运行的测地线感知注意力机制的几何变换器，以及基于神经微分方程的流形约束时序演化预测器。在四个公开数据集上的广泛评估表明，该方法显著优于当前最优方法，准确率提升4.6-4.8%，科恩卡帕系数提升6.2-10.2%，同时保持强跨被试泛化性能。几何方法揭示了符合神经生理学原理的有意义神经模式，确立几何约束对有效脑电基础模型至关重要。
原文摘要 · Abstract (English)
Existing EEG foundation models mainly treat neural signals as generic time series in Euclidean space, ignoring the intrinsic geometric structure of neural dynamics that constrains brain activity to low-dimensional manifolds. This fundamental mismatch between model assumptions and neural geometry limits representation quality and cross-subject generalization. ManifoldFormer addresses this limitation through a novel geometric deep learning framework that explicitly learns neural manifold representations. The architecture integrates three key innovations: a Riemannian VAE for manifold embedding that preserves geometric structure, a geometric Transformer with geodesic-aware attention mechanisms operating directly on neural manifolds, and a dynamics predictor leveraging neural ODEs for manifold-constrained temporal evolution. Extensive evaluation across four public datasets demonstrates substantial improvements over state-of-the-art methods, with 4.6-4.8% higher accuracy and 6.2-10.2% higher Cohen's Kappa, while maintaining robust cross-subject generalization. The geometric approach reveals meaningful neural patterns consistent with neurophysiological principles, establishing geometric constraints as essential for effective EEG foundation models.
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