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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Bibliographic Details

Published in:	Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) pp. 1 - 5
Main Authors:	Wang, Zirui, Song, Zhenxi, Guo, Yi, Liu, Yuxin, Xu, Guoyang, Zhang, Min, Zhang, Zhiguo
Format:	Conference Proceeding
Language:	English
Published:	IEEE 06.04.2025
Subjects:	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
Online Access:	Get full text
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