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

arXiv: https://arxiv.org/abs/2501.08139 · HTML: https://mlsanity.com/paper/2501.08139/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 30 references that are in the map.
- First posted on arXiv 14 Jan 2025.

## Builds on (most cited first, 9 of 9)

- [Contrastive Learning of Subject-Invariant EEG Representations for Cross-Subject Emotion Recognition](https://mlsanity.com/paper/2109.09559/) (2021)
- [Contrast Everything: A Hierarchical Contrastive Framework for Medical Time-Series](https://mlsanity.com/paper/2310.14017/) (2023)
- [MAtt: A Manifold Attention Network for EEG Decoding](https://mlsanity.com/paper/2210.01986/) (2022)
- [Tensor-CSPNet: A Novel Geometric Deep Learning Framework for Motor Imagery Classification](https://mlsanity.com/paper/2202.02472/) (2022)
- [Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG Signals](https://mlsanity.com/paper/2303.05798/) (2023)
- [Adaptive Gated Graph Convolutional Network for Explainable Diagnosis of Alzheimer's Disease using EEG Data](https://mlsanity.com/paper/2304.05874/) (2023)
- [Toward Fully-End-to-End Listened Speech Decoding from EEG Signals](https://mlsanity.com/paper/2406.08644/) (2024)
- [Seizure Detection using Least EEG Channels by Deep Convolutional Neural Network](https://mlsanity.com/paper/1901.05305/) (2019)
- [Deep Multiway Canonical Correlation Analysis for Multi-Subject EEG Normalization](https://mlsanity.com/paper/2103.06478/) (2021)

## Built on by (most cited first, 2 of 2)

- [EEG-SeeGraph: Interpreting functional connectivity disruptions in dementias via sparse-explanatory dynamic EEG-graph learning](https://mlsanity.com/paper/2603.16895/) (2026)
- [MPNet: A Robust and Efficient Manifold Pooling Network for Multi-Rhythm EEG Signal Decoding](https://mlsanity.com/paper/2605.05212/) (2026)

## Abstract (arXiv)

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.