通过几何感知变换提升跨被试脑机接口解码精度
Geometry-Aware Deep Congruence Networks for Manifold Learning in Cross-Subject Motor Imagery
- 引入几何感知共形变换，建模被试间协方差分布与方向差异
- 在跨被试运动想象任务中实现2-3%的准确率提升
- 适合关注脑机接口泛化能力与几何学习的研究者
跨被试运动想象解码是基于脑电的脑机接口中的核心挑战，主要源于被试间显著差异。现有方法利用黎曼几何，将脑电信号表示为对称正定（SPD）流形上的协方差矩阵。然而，这些方法多聚焦于流形表示，忽视了被试间协方差离散度与方向的差异。本文提出三种互补模型：判别性共形变换（DCT）、深度线性DCT（DLDCT）和深度DCT-UNet（DDCT-UNet）。这些模型既可作为下游分类器的流形对齐模块，也可作为端到端判别架构，通过交叉熵与定制逻辑回归头优化。在具有挑战性的跨被试运动想象基准测试中，解码性能持续提升，相较强基线高出2-3%。结果表明，几何感知共形学习能有效缓解脑电信号解码中的被试间差异。
原文摘要 · Abstract (English)
Cross-subject motor imagery decoding remains a fundamental challenge in EEG-based brain-computer interfaces due to substantial inter-subject variability. Recent approaches have leveraged Riemannian geometry by representing EEG signals as covariance matrices on the symmetric positive definite (SPD) manifold. However, existing methods primarily focus on manifold-based representations while largely overlooking subject-specific variations in covariance dispersion and orientation. In this work, we address these challenges through geometry-aware congruence transformations and propose three complementary models: (i) Discriminative Congruence Transform (DCT), (ii) Deep Linear DCT (DLDCT), and (iii) Deep DCT-UNet (DDCT-UNet). The proposed models are evaluated both as manifold alignment modules for downstream classifiers and as end-to-end discriminative architectures optimized via cross-entropy with a custom logistic regression head. Experiments on challenging cross-subject motor imagery benchmarks demonstrate consistent improvements in transductive decoding performance, achieving 2-3% higher accuracy than strong baselines. These results highlight the effectiveness of geometry-aware congruence learning for mitigating inter-subject variability in EEG decoding.
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