Stiefel-SPD Manifold Graph Convolution for End-to-End EEG Learning
Electroencephalographic (EEG) decoding relies heavily on second-order (covariance) structure that lives on the manifold of symmetric positive-definite (SPD) matrices. Conventional deep networks in Euclidean space ignore this geometry, distorting geodesic relations between covariances; classical Riem...
Kaydedildi:
| Asıl Yazarlar: | , , |
|---|---|
| Materyal Türü: | Artigo |
| Dil: | Inglês |
| Baskı/Yayın Bilgisi: |
IEEE
2026-01-01
|
| Seri Bilgileri: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| Konular: | |
| Online Erişim: | https://ieeexplore.ieee.org/document/11345236/ |
| Etiketler: |
Etiket eklenmemiş, İlk siz ekleyin!
|