# MAEEG: Masked Auto-encoder for EEG Representation Learning

arXiv: https://arxiv.org/abs/2211.02625 · HTML: https://mlsanity.com/paper/2211.02625/

## 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.

## Builds on (most cited first, 7 of 7)

- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [Masked Autoencoders that Listen](https://mlsanity.com/paper/2207.06405/) (2022)
- [BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG data](https://mlsanity.com/paper/2101.12037/) (2021)
- [Uncovering the structure of clinical EEG signals with self-supervised learning](https://mlsanity.com/paper/2007.16104/) (2020)
- [Speech Emotion: Investigating Model Representations, Multi-Task Learning and Knowledge Distillation](https://mlsanity.com/paper/2207.03334/) (2022)

## Built on by (most cited first, 30 of 62)

- [CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding](https://mlsanity.com/paper/2412.07236/) (2024)
- [REVE: A Foundation Model for EEG -- Adapting to Any Setup with Large-Scale Pretraining on 25,000 Subjects](https://mlsanity.com/paper/2510.21585/) (2025)
- [EEG2Rep: Enhancing Self-supervised EEG Representation Through Informative Masked Inputs](https://mlsanity.com/paper/2402.17772/) (2024)
- [Population Transformer: Learning Population-level Representations of Neural Activity](https://mlsanity.com/paper/2406.03044/) (2024)
- [Frequency-Aware Masked Autoencoders for Multimodal Pretraining on Biosignals](https://mlsanity.com/paper/2309.05927/) (2023)
- [LSM-2: Learning from Incomplete Wearable Sensor Data](https://mlsanity.com/paper/2506.05321/) (2025)
- [EEG2TEXT: Open Vocabulary EEG-to-Text Decoding with EEG Pre-Training and Multi-View Transformer](https://mlsanity.com/paper/2405.02165/) (2024)
- [Self-supervised Learning for Electroencephalogram: A Systematic Survey](https://mlsanity.com/paper/2401.05446/) (2024)
- [S-JEPA: towards seamless cross-dataset transfer through dynamic spatial attention](https://mlsanity.com/paper/2403.11772/) (2024)
- [Enhancing EEG-to-Text Decoding through Transferable Representations from Pre-trained Contrastive EEG-Text Masked Autoencoder](https://mlsanity.com/paper/2402.17433/) (2024)
- [Flow Matching Meets Biology and Life Science: A Survey](https://mlsanity.com/paper/2507.17731/) (2025)
- [DeeperBrain: A Neuro-Grounded EEG Foundation Model Towards Universal BCI](https://mlsanity.com/paper/2601.06134/) (2026)
- [ESTformer: Transformer utilising spatiotemporal dependencies for electroencephalogram super-resolution](https://mlsanity.com/paper/2312.10052/) (2023)
- [CREMA: A Contrastive Regularized Masked Autoencoder for Robust ECG Diagnostics across Clinical Domains](https://mlsanity.com/paper/2407.07110/) (2024)
- [Knowledge-guided EEG Representation Learning](https://mlsanity.com/paper/2403.03222/) (2024)
- [NeuroNet: A Novel Hybrid Self-Supervised Learning Framework for Sleep Stage Classification Using Single-Channel EEG](https://mlsanity.com/paper/2404.17585/) (2024)
- [Brain4FMs: A Benchmark of Foundation Models for Electrical Brain Signal](https://mlsanity.com/paper/2602.11558/) (2026)
- [EEG-based Cognitive Load Classification using Feature Masked Autoencoding and Emotion Transfer Learning](https://mlsanity.com/paper/2308.00246/) (2023)
- [Enhancing Listened Speech Decoding from EEG via Parallel Phoneme Sequence Prediction](https://mlsanity.com/paper/2501.04844/) (2025)
- [Is Limited Participant Diversity Impeding EEG-based Machine Learning?](https://mlsanity.com/paper/2503.13497/) (2025)
- [Global and Local Contrastive Learning for Joint Representations from Cardiac MRI and ECG](https://mlsanity.com/paper/2506.20683/) (2025)
- [Graph Adapter of EEG Foundation Models for Parameter Efficient Fine Tuning](https://mlsanity.com/paper/2411.16155/) (2024)
- [A Knowledge-Driven Cross-view Contrastive Learning for EEG Representation](https://mlsanity.com/paper/2310.03747/) (2023)
- [GEFM: Graph-Enhanced EEG Foundation Model](https://mlsanity.com/paper/2411.19507/) (2024)
- [BioSerenity-E1: a self-supervised EEG model for medical applications](https://mlsanity.com/paper/2503.10362/) (2025)
- [EEGDM: EEG Representation Learning via Generative Diffusion Model](https://mlsanity.com/paper/2508.14086/) (2025)
- [SAMBA: Toward a Long-Context EEG Foundation Model via Spatial Embedding and Differential Mamba](https://mlsanity.com/paper/2511.18571/) (2025)
- [Aerial Image Object Detection With Vision Transformer Detector (ViTDet)](https://mlsanity.com/paper/2301.12058/) (2023)
- [Review of Deep Representation Learning Techniques for Brain-Computer Interfaces and Recommendations](https://mlsanity.com/paper/2405.19345/) (2024)
- [A Survey of Spatio-Temporal EEG data Analysis: from Models to Applications](https://mlsanity.com/paper/2410.08224/) (2024)

## 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.