arXiv stat.ML
· Papers
Geometry-Aware Deep Congruence Networks for Manifold Learning in Cross-Subject Motor Imagery
arXiv:2511.18940v3 Announce Type: replace-cross 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 t