mlsanityData to 7 Oct 2026

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

arXiv 2111.08446 ↗First posted 3 Nov 2021eess.SP cs.AI cs.LGcited by 41

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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.

Key facts. Cited by 41 papers in the map (data through 7 Oct 2026). Builds on 72 of its 203 references that are in the map. First posted on arXiv 3 Nov 2021.

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72 of 203 references.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 7; 2024: 12; 2025: 12; 2026: 7.

41 papers.

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  1. 2024CBraMod: A Criss-Cross Brain Foundation Model for EEG Decodingcited by 188
  2. 2025CSBrain: A Cross-scale Spatiotemporal Brain Foundation Model for EEG Decodingcited by 46
  3. 2022Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Modelscited by 29
  4. 2023L-SeqSleepNet: Whole-cycle Long Sequence Modelling for Automatic Sleep Stagingcited by 21
  5. 2023BIOT: Cross-data Biosignal Learning in the Wildcited by 20
  6. 2024Brant-X: A Unified Physiological Signal Alignment Frameworkcited by 16
  7. 2022SleepPPG-Net: a deep learning algorithm for robust sleep staging from continuous photoplethysmographycited by 13
  8. 2024Promoting cross-modal representations to improve multimodal foundation models for physiological signalscited by 13
  9. 2022Automatic Sleep Scoring from Large-scale Multi-channel Pediatric EEGcited by 5
  10. 2024wav2sleep: A Unified Multi-Modal Approach to Sleep Stage Classification from Physiological Signalscited by 5
  11. 2026OmniEEG-Bench: A Standardized Evaluation Benchmark for EEG Foundation Modelscited by 5
  12. 2023Structure-Preserving Transformers for Sequences of SPD Matricescited by 4
29 more
  1. 2024SleepVST: Sleep Staging from Near-Infrared Video Signals using Pre-Trained Transformerscited by 4
  2. 2024FreqRISE: Explaining time series using frequency maskingcited by 4
  3. 2023Deep Learning-Enabled Sleep Staging From Vital Signs and Activity Measured Using a Near-Infrared Video Cameracited by 3
  4. 2023A novel dual-stream time-frequency contrastive pretext tasks framework for sleep stage classificationcited by 3
  5. 2024A Deep Generative Model for Five-Class Sleep Staging with Arbitrary Sensor Inputcited by 3
  6. 2025MSA-CNN: A Lightweight Multi-Scale CNN with Attention for Sleep Stage Classificationcited by 3
  7. 2023SleepEGAN: A GAN-enhanced Ensemble Deep Learning Model for Imbalanced Classification of Sleep Stagescited by 2
  8. 2024Evaluating the Influence of Temporal Context on Automatic Mouse Sleep Staging through the Application of Human Modelscited by 2
  9. 2023aSAGA: Automatic Sleep Analysis with Gray Areascited by 1
  10. 2024Label-Efficient Sleep Staging Using Transformers Pre-trained with Position Predictioncited by 1
  11. 2024Mamba-based Deep Learning Approach for Sleep Staging on a Wireless Multimodal Wearable System without Electroencephalographycited by 1
  12. 2025EEG Artifact Detection and Correction with Deep Autoencoderscited by 1
  13. 2025SPICED: A Synaptic Homeostasis-Inspired Framework for Unsupervised Continual EEG Decodingcited by 1
  14. 2025AnySleep: a channel-agnostic deep learning system for high-resolution sleep staging in multi-center cohortscited by 1
  15. 2026STDA-Net: Spectrogram-Based Domain Adaptation for cross-dataset Sleep Stage Classificationcited by 1
  16. 2026Rethinking Random Transformers as Adaptive Sequence Smoothers for Sleep Stagingcited by 1
  17. 2024Data-Efficient Sleep Staging with Synthetic Time Series Pretrainingcited by 0
  18. 2024Automatic Classification of Sleep Stages from EEG Signals Using Riemannian Metrics and Transformer Networkscited by 0
  19. 2025Multi-task deep-learning for sleep event detection and stage classificationcited by 0
  20. 2025From Sleep Staging to Spindle Detection: A Case Study on End-to-End Automated Sleep Analysiscited by 0
  21. 2025A Systematic Evaluation of Self-Supervised Learning for Label-Efficient Sleep Staging with Wearable EEGcited by 0
  22. 2025NAPS: Attention-Based Fusion of Heterogeneous Physiological Signalscited by 0
  23. 2025Estimating the Event-Related Potential from Few EEG Trialscited by 0
  24. 2025NeuroSketch: A Practical Design Recipe for Neural Decodingcited by 0
  25. 2025A Context-Aware Temporal Modeling through Unified Multi-Scale Temporal Encoding and Hierarchical Sequence Learning for Single-Channel EEG Sleep Stagingcited by 0
  26. 2026Unsupervised domain transfer: Overcoming signal degradation in sleep monitoring by increasing scoring realismcited by 0
  27. 2026A Sliced-Wasserstein Framework on Correlation Matrices for EEG Decodingcited by 0
  28. 2026NanoSleep: A Parameter-Efficient Hybrid Temporal Convolutional Network for Single-Channel Sleep Stage Classificationcited by 0
  29. 2026Learnable Time-Frequency Masks for Explaining Time-Series Classifierscited by 0