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

Sanjeev Manivannan; Chandra Shekar Lakshminarayan

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arXiv · 2511.18940

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

Sanjeev Manivannan·Chandra Shekar Lakshminarayan

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Abstract

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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BibTeX RIS

Sanjeev Manivannan, Chandra Shekar Lakshminarayan. 2026-07-09. Geometry-Aware Deep Congruence Networks for Manifold Learning in Cross-Subject Motor Imagery. https://arxiv.org/abs/2511.18940

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