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...
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| Autori principali: | , , |
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| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
IEEE
2026-01-01
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| Serie: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| Soggetti: | |
| Accesso online: | https://ieeexplore.ieee.org/document/11345236/ |
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