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

TL;DR

EEG-ReMinD introduces a self-supervised, geometry-aware method for EEG decoding that improves robustness and reduces label dependency, aiding neurodegenerative disease diagnosis.

Contribution

It presents a novel two-stage self-supervised approach leveraging Riemannian geometry and attention mechanisms for EEG analysis, addressing data sparsity and variability.

Findings

Outperforms existing methods on neurodegenerative disorder datasets.

Effectively handles corrupted EEG data with high accuracy.

Reduces reliance on extensive labeled data.

Abstract

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.