EEG-ReMinD: Enhancing Neurodegenerative EEG Decoding through Self-Supervised State Reconstruction-Primed Riemannian Dynamics

The development of EEG decoding algorithms confronts challenges such as data sparsity, subject variability, and the need for precise annotations, all of which are vital for advancing brain-computer interfaces and enhancing the diagnosis of diseases. To address these issues, we propose a novel two-st...

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Vydáno v:	Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) s. 1 - 5
Hlavní autoři:	Wang, Zirui, Song, Zhenxi, Guo, Yi, Liu, Yuxin, Xu, Guoyang, Zhang, Min, Zhang, Zhiguo
Médium:	Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	IEEE 06.04.2025
Témata:	Decoding Diseases EEG Electroencephalography Geometric Deep Learning Geometry Heuristic algorithms Neurodegenerative Disorders Riemannian Manifold Self-supervised Learning Signal processing Signal processing algorithms Speech processing Supervised learning Three-dimensional displays
ISSN:	2379-190X
On-line přístup:	Získat plný text
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