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Published on in Vol 2 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/46769, first published .
Self-Supervised Electroencephalogram Representation Learning for Automatic Sleep Staging: Model Development and Evaluation Study

Self-Supervised Electroencephalogram Representation Learning for Automatic Sleep Staging: Model Development and Evaluation Study

Self-Supervised Electroencephalogram Representation Learning for Automatic Sleep Staging: Model Development and Evaluation Study

Authors of this article:

Chaoqi Yang1 Author Orcid Image ;   Cao Xiao2 Author Orcid Image ;   M Brandon Westover3 Author Orcid Image ;   Jimeng Sun1 Author Orcid Image

Journals

  1. Wu H, Li S, Wu D. Motor Imagery Classification for Asynchronous EEG-Based Brain–Computer Interfaces. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2024;32:527 View
  2. Wang R, Chen Z. Large-scale foundation models and generative AI for BigData neuroscience. Neuroscience Research 2025;215:3 View
  3. Wang I, Lee C, Kim H, Kim D. Negative-Sample-Free Contrastive Self-Supervised Learning for Electroencephalogram-Based Motor Imagery Classification. IEEE Access 2024;12:132714 View
  4. Wang P, Zheng H, Dai S, Wang Y, Gu X, Wu Y, Wang X. A Comprehensive Survey on Emerging Techniques and Fusion Technologies in Spatio-Temporal EEG Data Analysis. Chinese Journal of Information Fusion 2024;1(3):183 View
  5. Huang Y, Chen Y, Xu S, Wu D, Wu X. Self-Supervised Learning with Adaptive Frequency-Time Attention Transformer for Seizure Prediction and Classification. Brain Sciences 2025;15(4):382 View
  6. Weng W, Gu Y, Guo S, Ma Y, Yang Z, Liu Y, Chen Y. Self-supervised Learning for Electroencephalogram: A Systematic Survey. ACM Computing Surveys 2025;57(12):1 View
  7. Wan Z, Yu Q, Dai W, Li S, Hong J. Data Generation for Enhancing EEG-Based Emotion Recognition: Extracting Time-Invariant and Subject-Invariant Components With Contrastive Learning. IEEE Transactions on Consumer Electronics 2025;71(1):1371 View
  8. Ghasemigarjan R, Mikaeili M, Kamaledin Setarehdan S, Saboori A. Enhancing EEG-based sleep staging efficiency with minimal channels through adversarial domain adaptation and active deep learning. Journal of Neural Engineering 2025;22(4):046043 View
  9. Chaibi S, Kachouri A. Toward Reliable Models for Distinguishing Epileptic High-Frequency Oscillations (HFOs) from Non-HFO Events Using LSTM and Pre-Trained OWL-ViT Vision–Language Framework. AI 2025;6(9):230 View
  10. Biedebach L, Ferreira-Santos D, Stefanos M, Lindhagen A, Pires G, Arnardóttir E, Islind A. Unsupervised machine learning in sleep research: a scoping review. SLEEPJ 2025;48(11) View
  11. Yao Y, Wang H, Chen L, Peng Y, Luo J. Foundation models for EEG decoding: current progress and prospective research. Journal of Neural Engineering 2025;22(6):061002 View
  12. Lou J, Zhang J, Li Z, Chen L, Feng E. DUFGNet: A dual-stream U-Net framework with frequency-guided channel attention and graph integration for epileptic seizure prediction. Neurocomputing 2026;667:132285 View
  13. Cong M. NeuroCognitor: Unified EEG-Language Framework for Cognitive Load Analysis via Instruction-Tuned Multi-Task Learning. IEEE Access 2025;13:201645 View
  14. Huang W, Wang Y, Cheng H, Xu W, Li T, Wu X, Xu H, Liao P, Cui Z, Zou Q, Gao J. A unified time-frequency foundation model for sleep decoding. Nature Communications 2026;17(1) View
  15. Li J, Chen X, Shen F, Chen J, Liu Y, Zhang D, Yuan Z, Zhao F, Li M, Yang Y. Deep Learning-Powered Electrical Brain Signals Analysis: Advancing Neurological Diagnostics. IEEE Reviews in Biomedical Engineering 2026;19:337 View
  16. Feng Y, Hu D, Hu W, Jiang T, Cao J. CL4CEA: A Clinical-Knowledge-Informed Augmentation for Contrastive Learning on Childhood Epilepsy Analysis. IEEE Signal Processing Letters 2026;33:823 View
  17. Li Z, Zheng W, Xu J, Lu Y, Lu B. Gram: A Large General EEG Model for Raw Data Classification and Restoration. IEEE Transactions on Affective Computing 2026;17(1):841 View
  18. Yang S, Zhang L, Cheng Y, Zheng Y, Zheng S, Guo J, Zheng L. STHMA: Decoupling Spatio-Temporal Dynamics in EEG via Hybrid State Space Modeling. Brain Sciences 2026;16(3):267 View
  19. R S, Navali V, Gajbhiye P, D S. Hybrid vision transformer–BiLSTM for EEG-based sleep apnea severity classification. Sleep Epidemiology 2026;6:100137 View

Books/Policy Documents

  1. Zakaria T, Langi A, Mahayana D, Anshori I. Health Information Science. View

Conference Proceedings

  1. Farooq M, Khan M. 2023 International Conference on Business Analytics for Technology and Security (ICBATS). Real-Time Human Body Sensors Data Classification using Supervised Machine Learning View
  2. Tran X, Nguyen Q, Le L, Do T, Lin C. Proceedings of the 1st International Workshop on Brain-Computer Interfaces (BCI) for Multimedia Understanding. EEG-Based Contrastive Learning Models For Object Perception Using Multisensory Image-Audio Stimuli View
  3. Jeong S, Jeon J, Suk H. 2025 13th International Conference on Brain-Computer Interface (BCI). Energy-Guided Topology Mamba for EEG-Based BCI View
  4. Lee J, Lee S, Tanade C, Nathan V, Thukral M, Zhou H, Chun K, Arcot Desai S. ICASSP 2026 - 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Multimodal Self-Supervised Learning for Wearable Sleep Staging Using Photoplethysmography and Accelerometer Signals View
  5. Wang Y, Xu L, Yu S, Tartaglione E, Nguyen V. ICASSP 2026 - 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). HFMCA: Orthonormal Feature Learning for EEG-Based Brain Decoding View
  6. Chao Y, Huang H, Xue J. 2026 IEEE International Conference on AI Engineering and Innovations (AIEI). Research on a Cross-Subject EEG Signal Analysis Framework Based on Self-Representation Learning View