{"total":105,"rows":[{"id":"2405.18765","title":"Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI","created":"2024-05-29","cited_by_count":327,"area":null},{"id":"2101.12037","title":"BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG data","created":"2021-01-28","cited_by_count":199,"area":null},{"id":"2412.07236","title":"CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding","created":"2024-12-10","cited_by_count":199,"area":null},{"id":"2208.12266","title":"Decoding speech perception from non-invasive brain recordings","created":"2022-08-25","cited_by_count":130,"area":null},{"id":"2312.05409","title":"Large-scale Training of Foundation Models for Wearable Biosignals","created":"2023-12-08","cited_by_count":95,"area":null},{"id":"2112.02472","title":"Augmentation-Free Self-Supervised Learning on Graphs","created":"2021-12-05","cited_by_count":94,"area":null},{"id":"2103.12676","title":"Self-supervised representation learning from 12-lead ECG data","created":"2021-03-23","cited_by_count":80,"area":null},{"id":"2104.08336","title":"Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis","created":"2021-04-16","cited_by_count":76,"area":null},{"id":"2302.14367","title":"BrainBERT: Self-supervised representation learning for intracranial recordings","created":"2023-02-28","cited_by_count":72,"area":null},{"id":"2106.09620","title":"Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICA","created":"2021-06-17","cited_by_count":64,"area":null},{"id":"2211.02625","title":"MAEEG: Masked Auto-encoder for EEG Representation Learning","created":"2022-10-27","cited_by_count":62,"area":null},{"id":"2310.16046","title":"A Unified, Scalable Framework for Neural Population Decoding","created":"2023-10-24","cited_by_count":61,"area":null},{"id":"2109.09559","title":"Contrastive Learning of Subject-Invariant EEG Representations for Cross-Subject Emotion Recognition","created":"2021-09-20","cited_by_count":60,"area":null},{"id":"2206.01323","title":"SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG","created":"2022-06-02","cited_by_count":53,"area":null},{"id":"2303.16535","title":"Nonlinear Independent Component Analysis for Principled Disentanglement in Unsupervised Deep Learning","created":"2023-03-29","cited_by_count":48,"area":null},{"id":"2111.08446","title":"Automatic Sleep Staging of EEG Signals: Recent Development, Challenges, and Future Directions","created":"2021-11-03","cited_by_count":43,"area":null},{"id":"2208.06616","title":"Self-supervised Contrastive Representation Learning for Semi-supervised Time-Series Classification","created":"2022-08-13","cited_by_count":39,"area":null},{"id":"2102.10106","title":"Mine Your Own vieW: Self-Supervised Learning Through Across-Sample Prediction","created":"2021-02-19","cited_by_count":36,"area":null},{"id":"2106.05142","title":"Neighborhood Contrastive Learning Applied to Online Patient Monitoring","created":"2021-06-09","cited_by_count":32,"area":null},{"id":"2110.15278","title":"Self-supervised EEG Representation Learning for Automatic Sleep Staging","created":"2021-10-27","cited_by_count":32,"area":null},{"id":"2507.11783","title":"EEG Foundation Models: A Critical Review of Current Progress and Future Directions","created":"2025-07-15","cited_by_count":32,"area":null},{"id":"2404.15319","title":"The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark","created":"2024-04-03","cited_by_count":29,"area":null},{"id":"2109.07839","title":"Self-supervised Contrastive Learning for EEG-based Sleep Staging","created":"2021-09-16","cited_by_count":27,"area":null},{"id":"2206.02353","title":"Beyond Just Vision: A Review on Self-Supervised Representation Learning on Multimodal and Temporal Data","created":"2022-06-06","cited_by_count":27,"area":null},{"id":"2306.13102","title":"MBrain: A Multi-channel Self-Supervised Learning Framework for Brain Signals","created":"2023-06-15","cited_by_count":26,"area":null},{"id":"2208.00467","title":"COCOA: Cross Modality Contrastive Learning for Sensor Data","created":"2022-07-31","cited_by_count":21,"area":null},{"id":"2310.04486","title":"T-Rep: Representation Learning for Time Series using Time-Embeddings","created":"2023-10-06","cited_by_count":19,"area":null},{"id":"2401.05446","title":"Self-supervised Learning for Electroencephalogram: A Systematic Survey","created":"2024-01-09","cited_by_count":19,"area":null},{"id":"2303.17611","title":"Transformer-based Self-supervised Multimodal Representation Learning for Wearable Emotion Recognition","created":"2023-03-29","cited_by_count":17,"area":null},{"id":"2209.11233","title":"Evaluating Latent Space Robustness and Uncertainty of EEG-ML Models under Realistic Distribution Shifts","created":"2022-09-22","cited_by_count":16,"area":null},{"id":"2302.06433","title":"Label-efficient Time Series Representation Learning: A Review","created":"2023-02-13","cited_by_count":16,"area":null},{"id":"2409.12454","title":"FoME: A Foundation Model for EEG using Adaptive Temporal-Lateral Attention Scaling","created":"2024-09-19","cited_by_count":16,"area":null},{"id":"2105.12916","title":"Robust learning from corrupted EEG with dynamic spatial filtering","created":"2021-05-27","cited_by_count":14,"area":null},{"id":"2204.12440","title":"Neuro-BERT: Rethinking Masked Autoencoding for Self-supervised Neurological Pretraining","created":"2022-04-20","cited_by_count":14,"area":null},{"id":"2210.06286","title":"Self-supervised Learning for Label-Efficient Sleep Stage Classification: A Comprehensive Evaluation","created":"2022-10-10","cited_by_count":14,"area":null},{"id":"2303.11338","title":"Towards Domain Generalization for ECG and EEG Classification: Algorithms and Benchmarks","created":"2023-03-20","cited_by_count":14,"area":null},{"id":"2410.16424","title":"Promoting cross-modal representations to improve multimodal foundation models for physiological signals","created":"2024-10-21","cited_by_count":13,"area":null},{"id":"2202.05400","title":"PARSE: Pairwise Alignment of Representations in Semi-Supervised EEG Learning for Emotion Recognition","created":"2022-02-11","cited_by_count":12,"area":null},{"id":"2112.06654","title":"Toward Open-World Electroencephalogram Decoding Via Deep Learning: A Comprehensive Survey","created":"2021-12-08","cited_by_count":11,"area":null},{"id":"2204.03272","title":"mulEEG: A Multi-View Representation Learning on EEG Signals","created":"2022-04-07","cited_by_count":11,"area":null},{"id":"2212.04604","title":"Localized Contrastive Learning on Graphs","created":"2022-12-08","cited_by_count":10,"area":null},{"id":"2509.24222","title":"Uni-NTFM: A Unified Foundation Model for EEG Signal Representation Learning","created":"2025-09-29","cited_by_count":10,"area":null},{"id":"2305.14035","title":"Can Self-Supervised Neural Representations Pre-Trained on Human Speech distinguish Animal Callers?","created":"2023-05-23","cited_by_count":9,"area":null},{"id":"2306.06994","title":"Correlated Time Series Self-Supervised Representation Learning via Spatiotemporal Bootstrapping","created":"2023-06-12","cited_by_count":9,"area":null},{"id":"2601.06134","title":"DeeperBrain: A Neuro-Grounded EEG Foundation Model Towards Universal BCI","created":"2026-01-05","cited_by_count":9,"area":null},{"id":"2308.02408","title":"Evaluating the structure of cognitive tasks with transfer learning","created":"2023-07-28","cited_by_count":8,"area":null},{"id":"2405.19532","title":"Contrasting Multiple Representations with the Multi-Marginal Matching Gap","created":"2024-05-29","cited_by_count":8,"area":null},{"id":"2111.02599","title":"Leveraging Time Irreversibility with Order-Contrastive Pre-training","created":"2021-11-04","cited_by_count":7,"area":null},{"id":"2110.09966","title":"SleepPriorCL: Contrastive Representation Learning with Prior Knowledge-based Positive Mining and Adaptive Temperature for Sleep Staging","created":"2021-10-15","cited_by_count":6,"area":null},{"id":"2208.00877","title":"Self-supervised Group Meiosis Contrastive Learning for EEG-Based Emotion Recognition","created":"2022-07-12","cited_by_count":6,"area":null},{"id":"2212.04902","title":"Self-Supervised PPG Representation Learning Shows High Inter-Subject Variability","created":"2022-12-07","cited_by_count":6,"area":null},{"id":"2401.05405","title":"SelfEEG: A Python library for Self-Supervised Learning in Electroencephalography","created":"2023-12-20","cited_by_count":5,"area":null},{"id":"2604.23091","title":"Channel Adaptation for EEG Foundation Models: A Systematic Benchmark Across Architectures, Tasks, and Training Regimes","created":"2026-04-25","cited_by_count":5,"area":null},{"id":"2206.07041","title":"Learning Behavior Representations Through Multi-Timescale Bootstrapping","created":"2022-06-14","cited_by_count":4,"area":null},{"id":"2310.03747","title":"A Knowledge-Driven Cross-view Contrastive Learning for EEG Representation","created":"2023-09-21","cited_by_count":4,"area":null},{"id":"2311.16849","title":"Identifiable Feature Learning for Spatial Data with Nonlinear ICA","created":"2023-11-28","cited_by_count":4,"area":null},{"id":"2408.08065","title":"SPEED: Scalable Preprocessing of EEG Data for Self-Supervised Learning","created":"2024-08-15","cited_by_count":4,"area":null},{"id":"2105.14278","title":"An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works","created":"2021-05-29","cited_by_count":3,"area":null},{"id":"2207.06911","title":"Improving self-supervised pretraining models for epileptic seizure detection from EEG data","created":"2022-06-28","cited_by_count":3,"area":null},{"id":"2307.06380","title":"Personalized Anomaly Detection in PPG Data using Representation Learning and Biometric Identification","created":"2023-07-12","cited_by_count":3,"area":null},{"id":"2312.09623","title":"A novel dual-stream time-frequency contrastive pretext tasks framework for sleep stage classification","created":"2023-12-15","cited_by_count":3,"area":null},{"id":"2404.09256","title":"GPT2MEG: Quantizing MEG for Autoregressive Generation","created":"2024-04-14","cited_by_count":3,"area":null},{"id":"2405.19345","title":"Review of Deep Representation Learning Techniques for Brain-Computer Interfaces and Recommendations","created":"2024-05-17","cited_by_count":3,"area":null},{"id":"2502.17213","title":"Deep Learning-Powered Electrical Brain Signals Analysis: Advancing Neurological Diagnostics","created":"2025-02-24","cited_by_count":3,"area":null},{"id":"2105.03037","title":"ConCAD: Contrastive Learning-based Cross Attention for Sleep Apnea Detection","created":"2021-05-07","cited_by_count":2,"area":null},{"id":"2207.13801","title":"Towards Sleep Scoring Generalization Through Self-Supervised Meta-Learning","created":"2022-07-27","cited_by_count":2,"area":null},{"id":"2210.14253","title":"Classification and Self-Supervised Regression of Arrhythmic ECG Signals Using Convolutional Neural Networks","created":"2022-10-25","cited_by_count":2,"area":null},{"id":"2301.09696","title":"Optimizing the Noise in Self-Supervised Learning: from Importance Sampling to Noise-Contrastive Estimation","created":"2023-01-23","cited_by_count":2,"area":null},{"id":"2310.08848","title":"Semi-Supervised End-To-End Contrastive Learning For Time Series Classification","created":"2023-10-13","cited_by_count":2,"area":null},{"id":"2312.00502","title":"Which Augmentation Should I Use? An Empirical Investigation of Augmentations for Self-Supervised Phonocardiogram Representation Learning","created":"2023-12-01","cited_by_count":2,"area":null},{"id":"2505.15355","title":"Decoding Phone Pairs from MEG Signals Across Speech Modalities","created":"2025-05-21","cited_by_count":2,"area":null},{"id":"2606.06647","title":"The Identity Trap in EEG Foundation Models: A Diagnostic Audit","created":"2026-06-04","cited_by_count":2,"area":null},{"id":"2203.02552","title":"A streamable large-scale clinical EEG dataset for Deep Learning","created":"2022-03-04","cited_by_count":1,"area":null},{"id":"2209.10992","title":"Modeling cognitive load as a self-supervised brain rate with electroencephalography and deep learning","created":"2022-09-21","cited_by_count":1,"area":null},{"id":"2307.13209","title":"Gait Cycle-Inspired Learning Strategy for Continuous Prediction of Knee Joint Trajectory from sEMG","created":"2023-07-25","cited_by_count":1,"area":null},{"id":"2506.16009","title":"Bridging Brain with Foundation Models through Self-Supervised Learning","created":"2025-06-19","cited_by_count":1,"area":null},{"id":"2506.22455","title":"Data Normalization Strategies for EEG Deep Learning","created":"2025-06-15","cited_by_count":1,"area":null},{"id":"2510.19829","title":"SSL-SE-EEG: A Framework for Robust Learning from Unlabeled EEG Data with Self-Supervised Learning and Squeeze-Excitation Networks","created":"2025-10-07","cited_by_count":1,"area":null},{"id":"2512.15830","title":"From Minutes to Days: Scaling Intracranial Speech Decoding with Supervised Pretraining","created":"2025-12-17","cited_by_count":1,"area":null},{"id":"2605.28792","title":"CaMBRAIN: Real-time, Continuous EEG Inference with Causal State Space Models","created":"2026-05-27","cited_by_count":1,"area":null},{"id":"2606.09605","title":"Next-Token Prediction Learns Generalisable Representations of Sleep Physiology","created":"2026-06-08","cited_by_count":1,"area":null},{"id":"2212.06413","title":"CropCat: Data Augmentation for Smoothing the Feature Distribution of EEG Signals","created":"2022-12-13","cited_by_count":0,"area":null},{"id":"2303.06386","title":"Scope and Arbitration in Machine Learning Clinical EEG Classification","created":"2023-03-11","cited_by_count":0,"area":null},{"id":"2303.10136","title":"MassNet: A Deep Learning Approach for Body Weight Extraction from A Single Pressure Image","created":"2023-03-17","cited_by_count":0,"area":null},{"id":"2304.09050","title":"Decoding Neural Activity to Assess Individual Latent State in Ecologically Valid Contexts","created":"2023-04-18","cited_by_count":0,"area":null},{"id":"2307.13181","title":"Neural Memory Decoding with EEG Data and Representation Learning","created":"2023-07-25","cited_by_count":0,"area":null},{"id":"2310.00964","title":"All by Myself: Learning Individualized Competitive Behaviour with a Contrastive Reinforcement Learning optimization","created":"2023-10-02","cited_by_count":0,"area":null},{"id":"2310.18377","title":"Large-scale Foundation Models and Generative AI for BigData Neuroscience","created":"2023-10-27","cited_by_count":0,"area":null},{"id":"2401.10283","title":"Window Stacking Meta-Models for Clinical EEG Classification","created":"2024-01-14","cited_by_count":0,"area":null},{"id":"2403.08592","title":"Data-Efficient Sleep Staging with Synthetic Time Series Pretraining","created":"2024-03-13","cited_by_count":0,"area":null},{"id":"2408.17118","title":"Efficient Estimation of Unique Components in Independent Component Analysis by Matrix Representation","created":"2024-08-30","cited_by_count":0,"area":null},{"id":"2410.19842","title":"A comprehensive evaluation of pretraining strategies for channel-agnostic contrastive self-supervision of biosignals","created":"2024-10-21","cited_by_count":0,"area":null},{"id":"2509.08188","title":"ArtifactGen: Benchmarking WGAN-GP vs Diffusion for Label-Aware EEG Artifact Synthesis","created":"2025-09-09","cited_by_count":0,"area":null},{"id":"2510.07960","title":"A Systematic Evaluation of Self-Supervised Learning for Label-Efficient Sleep Staging with Wearable EEG","created":"2025-10-09","cited_by_count":0,"area":null},{"id":"2511.11940","title":"Learning the relative composition of EEG signals using pairwise relative shift pretraining","created":"2025-11-14","cited_by_count":0,"area":null},{"id":"2512.13806","title":"EEG-D3: A Solution to the Hidden Overfitting Problem of Deep Learning Models","created":"2025-12-15","cited_by_count":0,"area":null},{"id":"2601.08549","title":"Contrastive and Multi-Task Learning on Noisy Brain Signals with Nonlinear Dynamical Signatures","created":"2026-01-13","cited_by_count":0,"area":null},{"id":"2603.12296","title":"Synthetic Data Generation for Brain-Computer Interfaces: Overview, Benchmarking, and Future Directions","created":"2026-03-11","cited_by_count":0,"area":null},{"id":"2605.14156","title":"Uncovering Trajectory and Topological Signatures in Multimodal Pediatric Sleep Embeddings","created":"2026-05-13","cited_by_count":0,"area":null},{"id":"2607.09543","title":"CoCoT-EEG: Contrastive-Pretrained Multiscale Convolutional Transformer for EEG Decoding","created":"2026-07-10","cited_by_count":0,"area":null}],"next_cursor":"100"}