Geometry-Aware Deep Congruence Networks for Manifold Learning in Cross-Subject Motor Imagery
arXiv:2511.18940v3 Announce Type: replace 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 co
This research builds on recent advancements in Riemannian geometry for EEG analysis, pushing the boundaries of brain-computer interface (BCI) technology, an area seeing rapid innovation.
Improving cross-subject motor imagery decoding is crucial for practical, user-independent BCI applications, impacting assistive technologies and cognitive enhancement.
The proposed 'Geometry-Aware Deep Congruence Networks' offer a more robust method for handling inter-subject variability in BCI, potentially making these systems more reliable and widely applicable.
- · Brain-Computer Interface Developers
- · Assistive Technologies Sector
- · AI/Machine Learning Researchers
- · Legacy BCI paradigms (non-manifold based)
- · Patients unable to utilize current BCI due to inter-subject variability
Enhanced reliability and broader applicability of motor imagery BCIs for communication and control.
Increased investment and research into personalized BCI solutions, leading to more sophisticated neuroprosthetics.
The development of BCI as a common interface, blurring lines between human cognition and AI-driven systems.
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