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

• URL: http://arxiv.org/abs/2501.08139v1
• Date: Tue, 14 Jan 2025 14:19:40 GMT
• Title: EEG-ReMinD: Enhancing Neurodegenerative EEG Decoding through Self-Supervised State Reconstruction-Primed Riemannian Dynamics
• Authors: Zirui Wang, Zhenxi Song, Yi Guo, Yuxin Liu, Guoyang Xu, Min Zhang, Zhiguo Zhang,
• Abstract summary: We propose a novel two-stage approach to EEG decoding called EEG-ReMinD.<n>EEG-ReMinD mitigates reliance on supervised learning and integrates inherent geometric features.<n>It efficiently handles EEG data corruptions and reduces the dependency on labels.
• Score: 24.57253767771542
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• RepSPD: Enhancing SPD Manifold Representation in EEGs via Dynamic Graphs [33.87495510816597]
  Decoding brain activity from electroencephalography (EEG) is crucial for neuroscience and clinical applications.<n>We propose RepSPD, a novel geometric deep learning (GDL)-based model.<n>We introduce a global bidirectional alignment strategy to reshape tangent-space embeddings, mitigating geometric distortions caused by curvature and thereby enhancing geometric consistency.
  arXiv  Detail & Related papers  (2026-02-26T13:22:19Z)
• WaveMind: Towards a Conversational EEG Foundation Model Aligned to Textual and Visual Modalities [55.00677513249723]
  EEG signals simultaneously encode both cognitive processes and intrinsic neural states.<n>We map EEG signals and their corresponding modalities into a unified semantic space to achieve generalized interpretation.<n>The resulting model demonstrates robust classification accuracy while supporting flexible, open-ended conversations.
  arXiv  Detail & Related papers  (2025-09-26T06:21:51Z)
• CodeBrain: Towards Decoupled Interpretability and Multi-Scale Architecture for EEG Foundation Model [52.466542039411515]
  EEG foundation models (EFMs) have emerged to address the scalability issues of task-specific models.<n>We present CodeBrain, a two-stage EFM designed to fill this gap.<n>In the first stage, we introduce the TFDual-Tokenizer, which decouples heterogeneous temporal and frequency EEG signals into discrete tokens.<n>In the second stage, we propose the multi-scale EEGSSM architecture, which combines structured global convolution with sliding window attention.
  arXiv  Detail & Related papers  (2025-06-10T17:20:39Z)
• BrainOmni: A Brain Foundation Model for Unified EEG and MEG Signals [50.76802709706976]
  This paper proposes Brain Omni, the first brain foundation model that generalises across heterogeneous EEG and MEG recordings.<n>To unify diverse data sources, we introduce BrainTokenizer, the first tokenizer that quantises neural brain activity into discrete representations.<n>A total of 1,997 hours of EEG and 656 hours of MEG data are curated and standardised from publicly available sources for pretraining.
  arXiv  Detail & Related papers  (2025-05-18T14:07:14Z)
• EEG2GAIT: A Hierarchical Graph Convolutional Network for EEG-based Gait Decoding [8.529597745689195]
  Decoding gait dynamics from EEG signals presents significant challenges due to the complex spatial dependencies of motor processes. We propose EEG2GAIT, a novel hierarchical graph-based model that captures multi-level spatial embeddings of EEG channels. We also contribute a new Gait-EEG dataset, consisting of synchronized EEG and lower-limb joint angle data collected from 50 participants over two lab visits.
  arXiv  Detail & Related papers  (2025-04-02T07:48:21Z)
• CognitionCapturer: Decoding Visual Stimuli From Human EEG Signal With Multimodal Information [61.1904164368732]
  We propose CognitionCapturer, a unified framework that fully leverages multimodal data to represent EEG signals.<n>Specifically, CognitionCapturer trains Modality Experts for each modality to extract cross-modal information from the EEG modality.<n>The framework does not require any fine-tuning of the generative models and can be extended to incorporate more modalities.
  arXiv  Detail & Related papers  (2024-12-13T16:27:54Z)
• Enhancing EEG Signal Generation through a Hybrid Approach Integrating Reinforcement Learning and Diffusion Models [6.102274021710727]
  This study introduces an innovative approach to the synthesis of Electroencephalogram (EEG) signals by integrating diffusion models with reinforcement learning. Our methodology enhances the generation of EEG signals with detailed temporal and spectral features, enriching the authenticity and diversity of synthetic datasets.
  arXiv  Detail & Related papers  (2024-09-14T07:22:31Z)
• RISE-iEEG: Robust to Inter-Subject Electrodes Implantation Variability iEEG Classifier [0.0]
  RISE-iEEG stands for Robust Inter-Subject Electrode Implantation Variability iEEG. We developed an iEEG decoder model that can be applied across multiple patients' data without requiring the coordinates of electrode for each patient. Our analysis shows that the performance of RISE-iEEG is 10% higher than that of HTNet and EEGNet in terms of F1 score.
  arXiv  Detail & Related papers  (2024-08-12T18:33:19Z)
• REST: Efficient and Accelerated EEG Seizure Analysis through Residual State Updates [54.96885726053036]
  This paper introduces a novel graph-based residual state update mechanism (REST) for real-time EEG signal analysis. By leveraging a combination of graph neural networks and recurrent structures, REST efficiently captures both non-Euclidean geometry and temporal dependencies within EEG data. Our model demonstrates high accuracy in both seizure detection and classification tasks.
  arXiv  Detail & Related papers  (2024-06-03T16:30:19Z)
• hvEEGNet: exploiting hierarchical VAEs on EEG data for neuroscience applications [3.031375888004876]
  Two main issues challenge the existing DL-based modeling methods for EEG. High variability between subjects and low signal-to-noise ratio make it difficult to ensure a good quality in the EEG data. We propose two variational autoencoder models, namely vEEGNet-ver3 and hvEEGNet, to target the problem of high-fidelity EEG reconstruction.
  arXiv  Detail & Related papers  (2023-11-20T15:36:31Z)
• fMRI-PTE: A Large-scale fMRI Pretrained Transformer Encoder for Multi-Subject Brain Activity Decoding [54.17776744076334]
  We propose fMRI-PTE, an innovative auto-encoder approach for fMRI pre-training. Our approach involves transforming fMRI signals into unified 2D representations, ensuring consistency in dimensions and preserving brain activity patterns. Our contributions encompass introducing fMRI-PTE, innovative data transformation, efficient training, a novel learning strategy, and the universal applicability of our approach.
  arXiv  Detail & Related papers  (2023-11-01T07:24:22Z)
• A Knowledge-Driven Cross-view Contrastive Learning for EEG Representation [48.85731427874065]
  This paper proposes a knowledge-driven cross-view contrastive learning framework (KDC2) to extract effective representations from EEG with limited labels. The KDC2 method creates scalp and neural views of EEG signals, simulating the internal and external representation of brain activity. By modeling prior neural knowledge based on neural information consistency theory, the proposed method extracts invariant and complementary neural knowledge to generate combined representations.
  arXiv  Detail & Related papers  (2023-09-21T08:53:51Z)
• DGSD: Dynamical Graph Self-Distillation for EEG-Based Auditory Spatial Attention Detection [49.196182908826565]
  Auditory Attention Detection (AAD) aims to detect target speaker from brain signals in a multi-speaker environment. Current approaches primarily rely on traditional convolutional neural network designed for processing Euclidean data like images. This paper proposes a dynamical graph self-distillation (DGSD) approach for AAD, which does not require speech stimuli as input.
  arXiv  Detail & Related papers  (2023-09-07T13:43:46Z)
• fMRI from EEG is only Deep Learning away: the use of interpretable DL to unravel EEG-fMRI relationships [68.8204255655161]
  We present an interpretable domain grounded solution to recover the activity of several subcortical regions from multichannel EEG data. We recover individual spatial and time-frequency patterns of scalp EEG predictive of the hemodynamic signal in the subcortical nuclei.
  arXiv  Detail & Related papers  (2022-10-23T15:11:37Z)
• MAtt: A Manifold Attention Network for EEG Decoding [0.966840768820136]
  We propose a novel geometric learning (GDL)-based model for EEG decoding, featuring a manifold attention network (mAtt) The evaluation of MAtt on both time-synchronous and -asyncronous EEG datasets suggests its superiority over other leading DL methods for general EEG decoding.
  arXiv  Detail & Related papers  (2022-10-05T02:26:31Z)
• Attention-based Graph ResNet for Motor Intent Detection from Raw EEG signals [8.775745069873558]
  In previous studies, decoding electroencephalography (EEG) signals has not considered the topological relationship of EEG electrodes. An attention-based graph residual network, a novel structure of Graph Convolutional Neural Network (GCN), was presented to detect human motor intents. Deep residual learning with a full-attention architecture was introduced to address the degradation problem concerning deeper networks in raw EEG motor imagery.
  arXiv  Detail & Related papers  (2020-06-25T09:29:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.