# Automatic Sleep Staging of EEG Signals: Recent Development, Challenges, and Future Directions

arXiv: https://arxiv.org/abs/2111.08446 · HTML: https://mlsanity.com/paper/2111.08446/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- Builds on 72 of its 203 references that are in the map.
- First posted on arXiv 3 Nov 2021.

## Builds on (most cited first, 30 of 72)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Neural Architecture Search with Reinforcement Learning](https://mlsanity.com/paper/1611.01578/) (2016)
- [Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1704.02685/) (2017)
- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design](https://mlsanity.com/paper/1807.11164/) (2018)
- [MnasNet: Platform-Aware Neural Architecture Search for Mobile](https://mlsanity.com/paper/1807.11626/) (2018)
- [Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images](https://mlsanity.com/paper/1412.1897/) (2014)
- [Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/1902.06162/) (2019)
- [Interpretable Explanations of Black Boxes by Meaningful Perturbation](https://mlsanity.com/paper/1704.03296/) (2017)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations](https://mlsanity.com/paper/1609.07061/) (2016)
- [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018)
- [U2-Net: Going Deeper with Nested U-Structure for Salient Object Detection](https://mlsanity.com/paper/2005.09007/) (2020)
- [Contrastive Adaptation Network for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1901.00976/) (2019)
- [A Survey of Unsupervised Deep Domain Adaptation](https://mlsanity.com/paper/1812.02849/) (2018)
- [Methods for Interpreting and Understanding Deep Neural Networks](https://mlsanity.com/paper/1706.07979/) (2017)
- [Unsupervised Domain Adaptation with Residual Transfer Networks](https://mlsanity.com/paper/1602.04433/) (2016)
- [Understanding Neural Networks Through Deep Visualization](https://mlsanity.com/paper/1506.06579/) (2015)
- [Domain Separation Networks](https://mlsanity.com/paper/1608.06019/) (2016)
- [Scalable and accurate deep learning for electronic health records](https://mlsanity.com/paper/1801.07860/) (2018)
- [Designing Neural Network Architectures using Reinforcement Learning](https://mlsanity.com/paper/1611.02167/) (2016)

## Built on by (most cited first, 30 of 43)

- [CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding](https://mlsanity.com/paper/2412.07236/) (2024)
- [CSBrain: A Cross-scale Spatiotemporal Brain Foundation Model for EEG Decoding](https://mlsanity.com/paper/2506.23075/) (2025)
- [Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Models](https://mlsanity.com/paper/2211.11176/) (2022)
- [L-SeqSleepNet: Whole-cycle Long Sequence Modelling for Automatic Sleep Staging](https://mlsanity.com/paper/2301.03441/) (2023)
- [BIOT: Cross-data Biosignal Learning in the Wild](https://mlsanity.com/paper/2305.10351/) (2023)
- [Brant-X: A Unified Physiological Signal Alignment Framework](https://mlsanity.com/paper/2409.00122/) (2024)
- [SleepPPG-Net: a deep learning algorithm for robust sleep staging from continuous photoplethysmography](https://mlsanity.com/paper/2202.05735/) (2022)
- [Promoting cross-modal representations to improve multimodal foundation models for physiological signals](https://mlsanity.com/paper/2410.16424/) (2024)
- [OmniEEG-Bench: A Standardized Evaluation Benchmark for EEG Foundation Models](https://mlsanity.com/paper/2606.00815/) (2026)
- [Automatic Sleep Scoring from Large-scale Multi-channel Pediatric EEG](https://mlsanity.com/paper/2207.06921/) (2022)
- [wav2sleep: A Unified Multi-Modal Approach to Sleep Stage Classification from Physiological Signals](https://mlsanity.com/paper/2411.04644/) (2024)
- [Structure-Preserving Transformers for Sequences of SPD Matrices](https://mlsanity.com/paper/2309.07579/) (2023)
- [SleepVST: Sleep Staging from Near-Infrared Video Signals using Pre-Trained Transformers](https://mlsanity.com/paper/2404.03831/) (2024)
- [FreqRISE: Explaining time series using frequency masking](https://mlsanity.com/paper/2406.13584/) (2024)
- [Deep Learning-Enabled Sleep Staging From Vital Signs and Activity Measured Using a Near-Infrared Video Camera](https://mlsanity.com/paper/2306.03711/) (2023)
- [A novel dual-stream time-frequency contrastive pretext tasks framework for sleep stage classification](https://mlsanity.com/paper/2312.09623/) (2023)
- [A Deep Generative Model for Five-Class Sleep Staging with Arbitrary Sensor Input](https://mlsanity.com/paper/2408.15253/) (2024)
- [MSA-CNN: A Lightweight Multi-Scale CNN with Attention for Sleep Stage Classification](https://mlsanity.com/paper/2501.02949/) (2025)
- [SleepEGAN: A GAN-enhanced Ensemble Deep Learning Model for Imbalanced Classification of Sleep Stages](https://mlsanity.com/paper/2307.05362/) (2023)
- [Evaluating the Influence of Temporal Context on Automatic Mouse Sleep Staging through the Application of Human Models](https://mlsanity.com/paper/2406.16911/) (2024)
- [EEG Artifact Detection and Correction with Deep Autoencoders](https://mlsanity.com/paper/2502.08686/) (2025)
- [aSAGA: Automatic Sleep Analysis with Gray Areas](https://mlsanity.com/paper/2310.02032/) (2023)
- [Label-Efficient Sleep Staging Using Transformers Pre-trained with Position Prediction](https://mlsanity.com/paper/2404.15308/) (2024)
- [Mamba-based Deep Learning Approach for Sleep Staging on a Wireless Multimodal Wearable System without Electroencephalography](https://mlsanity.com/paper/2412.15947/) (2024)
- [SPICED: A Synaptic Homeostasis-Inspired Framework for Unsupervised Continual EEG Decoding](https://mlsanity.com/paper/2509.17439/) (2025)
- [NAPS: Attention-Based Fusion of Heterogeneous Physiological Signals](https://mlsanity.com/paper/2511.03488/) (2025)
- [AnySleep: a channel-agnostic deep learning system for high-resolution sleep staging in multi-center cohorts](https://mlsanity.com/paper/2512.14461/) (2025)
- [STDA-Net: Spectrogram-Based Domain Adaptation for cross-dataset Sleep Stage Classification](https://mlsanity.com/paper/2605.06736/) (2026)
- [Rethinking Random Transformers as Adaptive Sequence Smoothers for Sleep Staging](https://mlsanity.com/paper/2605.09905/) (2026)
- [Data-Efficient Sleep Staging with Synthetic Time Series Pretraining](https://mlsanity.com/paper/2403.08592/) (2024)

## Abstract (arXiv)

Modern deep learning holds a great potential to transform clinical practice on human sleep. Teaching a machine to carry out routine tasks would be a tremendous reduction in workload for clinicians. Sleep staging, a fundamental step in sleep practice, is a suitable task for this and will be the focus in this article. Recently, automatic sleep staging systems have been trained to mimic manual scoring, leading to similar performance to human sleep experts, at least on scoring of healthy subjects. Despite tremendous progress, we have not seen automatic sleep scoring adopted widely in clinical environments. This review aims to give a shared view of the authors on the most recent state-of-the-art development in automatic sleep staging, the challenges that still need to be addressed, and the future directions for automatic sleep scoring to achieve clinical value.