# Self-supervised Contrastive Learning for EEG-based Sleep Staging

arXiv: https://arxiv.org/abs/2109.07839 · HTML: https://mlsanity.com/paper/2109.07839/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 35 references that are in the map.
- First posted on arXiv 16 Sep 2021.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Unsupervised Representation Learning by Predicting Image Rotations](https://mlsanity.com/paper/1803.07728/) (2018)
- [Colorful Image Colorization](https://mlsanity.com/paper/1603.08511/) (2016)
- [Learning Representations by Maximizing Mutual Information Across Views](https://mlsanity.com/paper/1906.00910/) (2019)
- [Revisiting Self-Supervised Visual Representation Learning](https://mlsanity.com/paper/1901.09005/) (2019)
- [DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG](https://mlsanity.com/paper/1703.04046/) (2017)
- [Uncovering the structure of clinical EEG signals with self-supervised learning](https://mlsanity.com/paper/2007.16104/) (2020)
- [U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging](https://mlsanity.com/paper/1910.11162/) (2019)
- [SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach](https://mlsanity.com/paper/1903.02108/) (2019)
- [Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification](https://mlsanity.com/paper/1805.06546/) (2018)
- [Federated Self-Supervised Learning of Multi-Sensor Representations for Embedded Intelligence](https://mlsanity.com/paper/2007.13018/) (2020)
- [Dreem Open Datasets: Multi-Scored Sleep Datasets to compare Human and Automated sleep staging](https://mlsanity.com/paper/1911.03221/) (2019)
- [Self-supervised Learning for ECG-based Emotion Recognition](https://mlsanity.com/paper/1910.07497/) (2019)
- [An Automated System for Epilepsy Detection using EEG Brain Signals based on Deep Learning Approach](https://mlsanity.com/paper/1801.05412/) (2018)
- [DOSED: a deep learning approach to detect multiple sleep micro-events in EEG signal](https://mlsanity.com/paper/1812.04079/) (2018)
- [RED: Deep Recurrent Neural Networks for Sleep EEG Event Detection](https://mlsanity.com/paper/2005.07795/) (2020)

## Built on by (most cited first, 27 of 27)

- [EEGFormer: Towards Transferable and Interpretable Large-Scale EEG Foundation Model](https://mlsanity.com/paper/2401.10278/) (2024)
- [Automatic Sleep Staging of EEG Signals: Recent Development, Challenges, and Future Directions](https://mlsanity.com/paper/2111.08446/) (2021)
- [Self-supervised Learning for Electroencephalogram: A Systematic Survey](https://mlsanity.com/paper/2401.05446/) (2024)
- [Transformer-based Self-supervised Multimodal Representation Learning for Wearable Emotion Recognition](https://mlsanity.com/paper/2303.17611/) (2023)
- [Label-efficient Time Series Representation Learning: A Review](https://mlsanity.com/paper/2302.06433/) (2023)
- [Self-supervised Learning for Label-Efficient Sleep Stage Classification: A Comprehensive Evaluation](https://mlsanity.com/paper/2210.06286/) (2022)
- [SleePyCo: Automatic Sleep Scoring with Feature Pyramid and Contrastive Learning](https://mlsanity.com/paper/2209.09452/) (2022)
- [Knowledge-guided EEG Representation Learning](https://mlsanity.com/paper/2403.03222/) (2024)
- [Self-supervised Group Meiosis Contrastive Learning for EEG-Based Emotion Recognition](https://mlsanity.com/paper/2208.00877/) (2022)
- [Guidelines for Augmentation Selection in Contrastive Learning for Time Series Classification](https://mlsanity.com/paper/2407.09336/) (2024)
- [The role of data partitioning on the performance of EEG-based deep learning models in supervised cross-subject analysis: a preliminary study](https://mlsanity.com/paper/2505.13021/) (2025)
- [From Video to EEG: Adapting Joint Embedding Predictive Architecture to Uncover Saptiotemporal Dynamics in Brain Signal Analysis](https://mlsanity.com/paper/2507.03633/) (2025)
- [A novel dual-stream time-frequency contrastive pretext tasks framework for sleep stage classification](https://mlsanity.com/paper/2312.09623/) (2023)
- [A Survey of Spatio-Temporal EEG data Analysis: from Models to Applications](https://mlsanity.com/paper/2410.08224/) (2024)
- [TS-OOD: Evaluating Time-Series Out-of-Distribution Detection and Prospective Directions for Progress](https://mlsanity.com/paper/2502.15901/) (2025)
- [Deep Learning-Powered Electrical Brain Signals Analysis: Advancing Neurological Diagnostics](https://mlsanity.com/paper/2502.17213/) (2025)
- [MVCNet: Multi-View Contrastive Network for Motor Imagery Classification](https://mlsanity.com/paper/2502.17482/) (2025)
- [Semi-Supervised End-To-End Contrastive Learning For Time Series Classification](https://mlsanity.com/paper/2310.08848/) (2023)
- [Domain Invariant Representation Learning and Sleep Dynamics Modeling for Automatic Sleep Staging](https://mlsanity.com/paper/2312.03196/) (2023)
- [Calibration-Free EEG-based Driver Drowsiness Detection with Online Test-Time Adaptation](https://mlsanity.com/paper/2511.22030/) (2025)
- [CropCat: Data Augmentation for Smoothing the Feature Distribution of EEG Signals](https://mlsanity.com/paper/2212.06413/) (2022)
- [Contrastive Learning for Sleep Staging based on Inter Subject Correlation](https://mlsanity.com/paper/2305.03178/) (2023)
- [MC2SleepNet: Multi-modal Cross-masking with Contrastive Learning for Sleep Stage Classification](https://mlsanity.com/paper/2502.17470/) (2025)
- [MCLPD:Multi-view Contrastive Learning for EEG-based PD Detection Across Datasets](https://mlsanity.com/paper/2508.14073/) (2025)
- [A Systematic Evaluation of Self-Supervised Learning for Label-Efficient Sleep Staging with Wearable EEG](https://mlsanity.com/paper/2510.07960/) (2025)
- [SleepBand: Single-Source Domain Generalization for Sleep Staging via Physiologically Structured Spectral Modeling](https://mlsanity.com/paper/2607.04851/) (2026)
- [Lightweight ML-Based Automatic Sleep Staging Framework with Constrained CNN and Mamba for Small-Sample EEG Datasets](https://mlsanity.com/paper/2607.04934/) (2026)

## Abstract (arXiv)

EEG signals are usually simple to obtain but expensive to label. Although supervised learning has been widely used in the field of EEG signal analysis, its generalization performance is limited by the amount of annotated data. Self-supervised learning (SSL), as a popular learning paradigm in computer vision (CV) and natural language processing (NLP), can employ unlabeled data to make up for the data shortage of supervised learning. In this paper, we propose a self-supervised contrastive learning method of EEG signals for sleep stage classification. During the training process, we set up a pretext task for the network in order to match the right transformation pairs generated from EEG signals. In this way, the network improves the representation ability by learning the general features of EEG signals. The robustness of the network also gets improved in dealing with diverse data, that is, extracting constant features from changing data. In detail, the network's performance depends on the choice of transformations and the amount of unlabeled data used in the training process of self-supervised learning. Empirical evaluations on the Sleep-edf dataset demonstrate the competitive performance of our method on sleep staging (88.16% accuracy and 81.96% F1 score) and verify the effectiveness of SSL strategy for EEG signal analysis in limited labeled data regimes. All codes are provided publicly online.