Geometry-Aware Deep Congruence Networks for Manifold Learning in Cross-Subject Motor Imagery

• URL: http://arxiv.org/abs/2511.18940v1
• Date: Mon, 24 Nov 2025 09:46:55 GMT
• Title: Geometry-Aware Deep Congruence Networks for Manifold Learning in Cross-Subject Motor Imagery
• Abstract summary: Cross-subject motor-imagery decoding remains a major challenge in EEG-based brain-computer interfaces.<n>We introduce novel geometry-aware preprocessing modules and deep congruence networks.<n>Our framework improves cross-subject accuracy by 3-4% over the strongest classical baselines.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cross-subject motor-imagery decoding remains a major challenge in EEG-based brain-computer interfaces due to strong subject variability and the curved geometry of covariance matrices on the symmetric positive definite (SPD) manifold. We address the zero-shot cross-subject setting, where no target-subject labels or adaptation are allowed, by introducing novel geometry-aware preprocessing modules and deep congruence networks that operate directly on SPD covariance matrices. Our preprocessing modules, DCR and RiFU, extend Riemannian Alignment by improving action separation while reducing subject-specific distortions. We further propose two manifold classifiers, SPD-DCNet and RiFUNet, which use hierarchical congruence transforms to learn discriminative, subject-invariant covariance representations. On the BCI-IV 2a benchmark, our framework improves cross-subject accuracy by 3-4% over the strongest classical baselines, demonstrating the value of geometry-aware transformations for robust EEG decoding.

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