EEG-ReMinD: Enhancing Neurodegenerative EEG Decoding through Self-Supervised State Reconstruction-Primed Riemannian Dynamics
This work addresses data sparsity and annotation needs in EEG decoding for brain-computer interfaces and disease diagnosis, representing an incremental advancement in the field.
The paper tackles challenges in EEG decoding for neurodegenerative diseases by proposing EEG-ReMinD, a two-stage approach that reduces reliance on supervised learning and integrates geometric features. Results show enhanced performance on both intact and corrupted datasets from two neurodegenerative disorders, though specific numerical improvements are not provided.
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.