Learning the relative composition of EEG signals using pairwise relative shift pretraining

• URL: http://arxiv.org/abs/2511.11940v1
• Date: Fri, 14 Nov 2025 23:30:37 GMT
• Title: Learning the relative composition of EEG signals using pairwise relative shift pretraining
• Abstract summary: Self-supervised learning (SSL) offers a promising approach for learning electroencephalography (EEG) representations from unlabeled data.<n>We introduce PAirwise Relative Shift or PARS pretraining, a novel pretext task that predicts relative temporal shifts between randomly sampled EEG window pairs.<n>We demonstrate that PARS-pretrained transformers consistently outperform existing pretraining strategies in label-efficient and transfer learning settings.
• Score: 4.9639655603300215
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Self-supervised learning (SSL) offers a promising approach for learning electroencephalography (EEG) representations from unlabeled data, reducing the need for expensive annotations for clinical applications like sleep staging and seizure detection. While current EEG SSL methods predominantly use masked reconstruction strategies like masked autoencoders (MAE) that capture local temporal patterns, position prediction pretraining remains underexplored despite its potential to learn long-range dependencies in neural signals. We introduce PAirwise Relative Shift or PARS pretraining, a novel pretext task that predicts relative temporal shifts between randomly sampled EEG window pairs. Unlike reconstruction-based methods that focus on local pattern recovery, PARS encourages encoders to capture relative temporal composition and long-range dependencies inherent in neural signals. Through comprehensive evaluation on various EEG decoding tasks, we demonstrate that PARS-pretrained transformers consistently outperform existing pretraining strategies in label-efficient and transfer learning settings, establishing a new paradigm for self-supervised EEG representation learning.

Related papers

• Monitoring Neural Training with Topology: A Footprint-Predictable Collapse Index [51.56484100374058]
  We propose an online, topology-aware monitor for evolving neural representations that couples Modular Morse Homology Maintenance (MMHM) with a composite Collapse Index (CI)<n>Instead of rebuilding complexes each epoch, we apply sparse edits at a fixed scale and maintain a discrete Morse matching, yielding fast, incremental updates.
  arXiv  Detail & Related papers  (2026-04-28T19:21:21Z)
• CoRe-ECG: Advancing Self-Supervised Representation Learning for 12-Lead ECG via Contrastive and Reconstructive Synergy [37.81373064028234]
  We propose CoRe-ECG, a unified contrastive and reconstructive pretraining paradigm.<n>CoRe-ECG aligns global representations during reconstruction, enabling instance-level discriminative signals to guide local waveform recovery.<n>Our method achieves state-of-the-art performance across multiple downstream ECG datasets.
  arXiv  Detail & Related papers  (2026-04-13T11:56:43Z)
• Epileptic Seizure Prediction Using Patient-Adaptive Transformer Networks [0.0]
  This paper presents a patient-adaptive transformer framework for short-horizon seizure forecasting.<n>The proposed approach employs a two-stage training strategy: self-supervised pretraining is first used to learn general EEG temporal representations.<n>To enable transformer-based sequence learning, multichannel EEG signals are processed using noise-aware preprocessing.
  arXiv  Detail & Related papers  (2026-03-26T23:48:10Z)
• Self-Supervised Learning via Flow-Guided Neural Operator on Time-Series Data [57.85958428020496]
  Flow-Guided Neural Operator (FGNO) is a novel framework combining operator learning with flow matching for SSL training.<n>FGNO learns mappings in functional spaces by using Short-Time Fourier Transform to unify different time resolutions.<n>Unlike prior generative SSL methods that use noisy inputs during inference, we propose using clean inputs for representation extraction while learning representations with noise.
  arXiv  Detail & Related papers  (2026-02-12T18:54:57Z)
• RAICL: Retrieval-Augmented In-Context Learning for Vision-Language-Model Based EEG Seizure Detection [12.189806103703887]
  We propose a paradigm shift from conventional signal-based decoding by leveraging large-scale vision-language models (VLMs) to analyze EEG waveform plots.<n>To address the inherent non-stationarity of EEG signals, we introduce a Retrieval-Augmented In-Context Learning (RAICL) approach.
  arXiv  Detail & Related papers  (2026-01-25T13:58:31Z)
• EEG-Titans: Long-Horizon Seizure Forecasting via Dual-Branch Attention and Neural Memory [0.0]
  We propose a dual-branch EEG architecture that incorporates modern neural memory mechanism for long-context modeling.<n>EEG-Titans achieves 99.46% average segment-level sensitivity across 18 subjects.<n>Results indicate that memory-augmented long-context modeling can provide robust seizure forecasting under clinically constrained evaluation.
  arXiv  Detail & Related papers  (2026-01-20T09:03:49Z)
• SPECTRE: Spectral Pre-training Embeddings with Cylindrical Temporal Rotary Position Encoding for Fine-Grained sEMG-Based Movement Decoding [16.992187199187764]
  Decoding fine-grained movement from non-invasive surface Electromyography (sEMG) is a challenge for prosthetic control due to signal non-stationarity and low signal-to-noise ratios.<n>We introduce SPECTRE, a domain-specific SSL framework to overcome these limitations.<n>We show that SPECTRE establishes a new state-of-the-art for movement decoding, significantly outperforming both supervised baselines and generic SSL approaches.
  arXiv  Detail & Related papers  (2025-12-27T05:55:06Z)
• rETF-semiSL: Semi-Supervised Learning for Neural Collapse in Temporal Data [44.17657834678967]
  We propose a novel semi-supervised pre-training strategy to enforce latent representations that satisfy the Neural Collapse phenomenon.<n>We show that our method significantly outperforms previous pretext tasks when applied to LSTMs, transformers, and state-space models.
  arXiv  Detail & Related papers  (2025-08-13T19:16:47Z)
• Zero-Shot EEG-to-Gait Decoding via Phase-Aware Representation Learning [9.49131859415923]
  We propose NeuroDyGait, a domain-generalizable EEG-to-motion decoding framework.<n>It uses structured contrastive representation learning and relational domain modeling to achieve semantic alignment between EEG and motion embeddings.<n>It achieves zero-shot motion prediction for unseen individuals without requiring adaptation and superior performance in cross-subject gait decoding on benchmark datasets.
  arXiv  Detail & Related papers  (2025-06-24T06:03:49Z)
• PSDNorm: Test-Time Temporal Normalization for Deep Learning in Sleep Staging [63.05435596565677]
  We propose PSDNorm that leverages Monge mapping and temporal context to normalize feature maps in deep learning models for signals.<n> PSDNorm achieves state-of-the-art performance on unseen left-out datasets while being 4-times more data-efficient than BatchNorm.
  arXiv  Detail & Related papers  (2025-03-06T16:20:25Z)
• 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)
• Joint-Embedding Masked Autoencoder for Self-supervised Learning of Dynamic Functional Connectivity from the Human Brain [16.62883475350025]
  Graph Neural Networks (GNNs) have shown promise in learning dynamic functional connectivity for distinguishing phenotypes from human brain networks.<n>We introduce the Spatio-Temporal Joint Embedding Masked Autoencoder (ST-JEMA), drawing inspiration from the Joint Embedding Predictive Architecture (JEPA) in computer vision.
  arXiv  Detail & Related papers  (2024-03-11T04:49:41Z)
• Contrastive-Signal-Dependent Plasticity: Self-Supervised Learning in Spiking Neural Circuits [61.94533459151743]
  This work addresses the challenge of designing neurobiologically-motivated schemes for adjusting the synapses of spiking networks. Our experimental simulations demonstrate a consistent advantage over other biologically-plausible approaches when training recurrent spiking networks.
  arXiv  Detail & Related papers  (2023-03-30T02:40:28Z)
• ETLP: Event-based Three-factor Local Plasticity for online learning with neuromorphic hardware [105.54048699217668]
  We show a competitive performance in accuracy with a clear advantage in the computational complexity for Event-Based Three-factor Local Plasticity (ETLP) We also show that when using local plasticity, threshold adaptation in spiking neurons and a recurrent topology are necessary to learntemporal patterns with a rich temporal structure.
  arXiv  Detail & Related papers  (2023-01-19T19:45:42Z)
• The Predictive Forward-Forward Algorithm [79.07468367923619]
  We propose the predictive forward-forward (PFF) algorithm for conducting credit assignment in neural systems. We design a novel, dynamic recurrent neural system that learns a directed generative circuit jointly and simultaneously with a representation circuit. PFF efficiently learns to propagate learning signals and updates synapses with forward passes only.
  arXiv  Detail & Related papers  (2023-01-04T05:34:48Z)
• DriPP: Driven Point Processes to Model Stimuli Induced Patterns in M/EEG Signals [62.997667081978825]
  We develop a novel statistical point process model-called driven temporal point processes (DriPP) We derive a fast and principled expectation-maximization (EM) algorithm to estimate the parameters of this model. Results on standard MEG datasets demonstrate that our methodology reveals event-related neural responses.
  arXiv  Detail & Related papers  (2021-12-08T13:07:21Z)
• EEG-based Cross-Subject Driver Drowsiness Recognition with an Interpretable Convolutional Neural Network [0.0]
  We develop a novel convolutional neural network combined with an interpretation technique that allows sample-wise analysis of important features for classification. Results show that the model achieves an average accuracy of 78.35% on 11 subjects for leave-one-out cross-subject recognition.
  arXiv  Detail & Related papers  (2021-05-30T14:47:20Z)
• Uncovering the structure of clinical EEG signals with self-supervised learning [64.4754948595556]
  Supervised learning paradigms are often limited by the amount of labeled data that is available. This phenomenon is particularly problematic in clinically-relevant data, such as electroencephalography (EEG) By extracting information from unlabeled data, it might be possible to reach competitive performance with deep neural networks.
  arXiv  Detail & Related papers  (2020-07-31T14:34:47Z)

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.