mlsanityData to 14 Oct 2026

MAEEG: Masked Auto-encoder for EEG Representation Learning

arXiv 2211.02625 ↗First posted 27 Oct 2022eess.SP cs.LGcited by 62

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Abstract (arXiv)

Decoding information from bio-signals such as EEG, using machine learning has been a challenge due to the small data-sets and difficulty to obtain labels. We propose a reconstruction-based self-supervised learning model, the masked auto-encoder for EEG (MAEEG), for learning EEG representations by learning to reconstruct the masked EEG features using a transformer architecture. We found that MAEEG can learn representations that significantly improve sleep stage classification (~5% accuracy increase) when only a small number of labels are given. We also found that input sample lengths and different ways of masking during reconstruction-based SSL pretraining have a huge effect on downstream model performance. Specifically, learning to reconstruct a larger proportion and more concentrated masked signal results in better performance on sleep classification. Our findings provide insight into how reconstruction-based SSL could help representation learning for EEG.

Key facts. Cited by 62 papers in the map (data through 14 Oct 2026). Builds on 7 of its 10 references that are in the map. First posted on arXiv 27 Oct 2022.

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7 of 10 references.

References · 7

  1. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,403
  2. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,905
  3. 2016EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfacescited by 628
  4. 2022Masked Autoencoders that Listencited by 273
  5. 2021BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG datacited by 199
  6. 2020Uncovering the structure of clinical EEG signals with self-supervised learningcited by 109
  7. 2022Speech Emotion: Investigating Model Representations, Multi-Task Learning and Knowledge Distillationcited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 6; 2024: 18; 2025: 24; 2026: 14.

62 papers.

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  1. 2024CBraMod: A Criss-Cross Brain Foundation Model for EEG Decodingcited by 199
  2. 2025REVE: A Foundation Model for EEG -- Adapting to Any Setup with Large-Scale Pretraining on 25,000 Subjectscited by 80
  3. 2024EEG2Rep: Enhancing Self-supervised EEG Representation Through Informative Masked Inputscited by 50
  4. 2024Population Transformer: Learning Population-level Representations of Neural Activitycited by 26
  5. 2023Frequency-Aware Masked Autoencoders for Multimodal Pretraining on Biosignalscited by 23
  6. 2025LSM-2: Learning from Incomplete Wearable Sensor Datacited by 23
  7. 2024EEG2TEXT: Open Vocabulary EEG-to-Text Decoding with EEG Pre-Training and Multi-View Transformercited by 20
  8. 2024Self-supervised Learning for Electroencephalogram: A Systematic Surveycited by 19
  9. 2024S-JEPA: towards seamless cross-dataset transfer through dynamic spatial attentioncited by 16
  10. 2024Enhancing EEG-to-Text Decoding through Transferable Representations from Pre-trained Contrastive EEG-Text Masked Autoencodercited by 15
  11. 2025Flow Matching Meets Biology and Life Science: A Surveycited by 14
  12. 2026DeeperBrain: A Neuro-Grounded EEG Foundation Model Towards Universal BCIcited by 9
38 more
  1. 2023ESTformer: Transformer utilising spatiotemporal dependencies for electroencephalogram super-resolutioncited by 8
  2. 2024CREMA: A Contrastive Regularized Masked Autoencoder for Robust ECG Diagnostics across Clinical Domainscited by 8
  3. 2024Knowledge-guided EEG Representation Learningcited by 7
  4. 2024NeuroNet: A Novel Hybrid Self-Supervised Learning Framework for Sleep Stage Classification Using Single-Channel EEGcited by 7
  5. 2026Brain4FMs: A Benchmark of Foundation Models for Electrical Brain Signalcited by 7
  6. 2023EEG-based Cognitive Load Classification using Feature Masked Autoencoding and Emotion Transfer Learningcited by 6
  7. 2025Enhancing Listened Speech Decoding from EEG via Parallel Phoneme Sequence Predictioncited by 6
  8. 2025Is Limited Participant Diversity Impeding EEG-based Machine Learning?cited by 6
  9. 2025Global and Local Contrastive Learning for Joint Representations from Cardiac MRI and ECGcited by 6
  10. 2024Graph Adapter of EEG Foundation Models for Parameter Efficient Fine Tuningcited by 5
  11. 2023A Knowledge-Driven Cross-view Contrastive Learning for EEG Representationcited by 4
  12. 2024GEFM: Graph-Enhanced EEG Foundation Modelcited by 4
  13. 2025BioSerenity-E1: a self-supervised EEG model for medical applicationscited by 4
  14. 2025EEGDM: EEG Representation Learning via Generative Diffusion Modelcited by 4
  15. 2025SAMBA: Toward a Long-Context EEG Foundation Model via Spatial Embedding and Differential Mambacited by 4
  16. 2023Aerial Image Object Detection With Vision Transformer Detector (ViTDet)cited by 3
  17. 2024Review of Deep Representation Learning Techniques for Brain-Computer Interfaces and Recommendationscited by 3
  18. 2024A Survey of Spatio-Temporal EEG data Analysis: from Models to Applicationscited by 3
  19. 2025Deep Learning-Powered Electrical Brain Signals Analysis: Advancing Neurological Diagnosticscited by 3
  20. 2025Transformer-based EEG Decoding: A Surveycited by 3
  21. 2026A robust PPG foundation model using multimodal physiological supervisioncited by 3
  22. 2024CiTrus: Squeezing Extra Performance out of Low-data Bio-signal Transfer Learningcited by 2
  23. 2025Few-Shot Radar Signal Recognition through Self-Supervised Learning and Radio Frequency Domain Adaptationcited by 2
  24. 2025An Efficient Self-Supervised Framework for Long-Sequence EEG Modelingcited by 2
  25. 2025DIVER-0 : A Fully Channel Equivariant EEG Foundation Modelcited by 2
  26. 2024Label-Efficient Sleep Staging Using Transformers Pre-trained with Position Predictioncited by 1
  27. 2024Evaluating Fast Adaptability of Neural Networks for Brain-Computer Interfacecited by 1
  28. 2025Bridging Brain with Foundation Models through Self-Supervised Learningcited by 1
  29. 2025EEGDM: Learning EEG Representation with Latent Diffusion Modelcited by 1
  30. 2025Learning Time-Scale Invariant Population-Level Neural Representationscited by 1
  31. 2025SPECTRE: Spectral Pre-training Embeddings with Cylindrical Temporal Rotary Position Encoding for Fine-Grained sEMG-Based Movement Decodingcited by 1
  32. 2026SCOPE: Structured Prototype-Guided Adaptation for EEG Foundation Models with Limited Labelscited by 1
  33. 2026CLEF: EEG Foundation Model for Learning Clinical Semanticscited by 1
  34. 2026Aperiodic and Low-Frequency Spectral Bias in Reconstruction based EEG Foundation Modelscited by 1
  35. 2026Understanding and Correcting Low-Frequency Bias in EEG Foundation Modelcited by 1
  36. 2023Neural Memory Decoding with EEG Data and Representation Learningcited by 0
  37. 2024A Masked Semi-Supervised Learning Approach for Otago Micro Labels Recognitioncited by 0
  38. 2025MC2SleepNet: Multi-modal Cross-masking with Contrastive Learning for Sleep Stage Classificationcited by 0

50 most cited of 62 All 62 (JSON)