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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Үндсэн зохиолчид:	Imad Eddine Tibermacine, Samuele Russo, Christian Napoli
Формат:	Artigo
Хэл сонгох:	Inglês
Хэвлэсэн:	IEEE 2026-01-01
Цуврал:	IEEE Transactions on Neural Systems and Rehabilitation Engineering
Нөхцлүүд:	EEG Riemannian geometry Stiefel manifold graph convolution
Онлайн хандалт:	https://ieeexplore.ieee.org/document/11345236/
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