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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Vydané v:Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) s. 1 - 5
Hlavní autori: Wang, Zirui, Song, Zhenxi, Guo, Yi, Liu, Yuxin, Xu, Guoyang, Zhang, Min, Zhang, Zhiguo
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 06.04.2025
Predmet:
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
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Shrnutí: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-stage approach named Self-Supervised State Reconstruction-Primed Riemannian Dynamics (EEG-ReMinD), which mitigates reliance on supervised learning and integrates inherent geometric features. This approach efficiently handles EEG data corruptions and reduces the dependency on labels. EEG-ReMinD utilizes self-supervised and geometric learning techniques, along with an attention mechanism, to analyze the temporal dynamics of EEG features within the framework of Riemannian geometry, referred to as Riemannian dynamics. Comparative analyses on both intact and corrupted datasets from two different neurodegenerative disorders underscore the enhanced performance of EEG-ReMinD.
ISSN:2379-190X
DOI:10.1109/ICASSP49660.2025.10887579