mlsanityData to 7 Oct 2026

Self-supervised Learning for Electroencephalogram: A Systematic Survey

arXiv 2401.05446 ↗First posted 9 Jan 2024eess.SP cs.AI cs.LGcited by 19

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Abstract (arXiv)

Electroencephalogram (EEG) is a non-invasive technique to record bioelectrical signals. Integrating supervised deep learning techniques with EEG signals has recently facilitated automatic analysis across diverse EEG-based tasks. However, the label issues of EEG signals have constrained the development of EEG-based deep models. Obtaining EEG annotations is difficult that requires domain experts to guide collection and labeling, and the variability of EEG signals among different subjects causes significant label shifts. To solve the above challenges, self-supervised learning (SSL) has been proposed to extract representations from unlabeled samples through well-designed pretext tasks. This paper concentrates on integrating SSL frameworks with temporal EEG signals to achieve efficient representation and proposes a systematic review of the SSL for EEG signals. In this paper, 1) we introduce the concept and theory of self-supervised learning and typical SSL frameworks. 2) We provide a comprehensive review of SSL for EEG analysis, including taxonomy, methodology, and technique details of the existing EEG-based SSL frameworks, and discuss the difference between these methods. 3) We investigate the adaptation of the SSL approach to various downstream tasks, including the task description and related benchmark datasets. 4) Finally, we discuss the potential directions for future SSL-EEG research.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 68 of its 208 references that are in the map. First posted on arXiv 9 Jan 2024.

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68 of 208 references.

References · 68

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 9; 2026: 8.

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  1. 2024EEG2Rep: Enhancing Self-supervised EEG Representation Through Informative Masked Inputscited by 48
  2. 2025EEG Foundation Models: A Critical Review of Current Progress and Future Directionscited by 30
  3. 2026LLM as Clinical Graph Structure Refiner: Enhancing Representation Learning in EEG Seizure Diagnosiscited by 6
  4. 2025A Survey on Bridging EEG Signals and Generative AI: From Image and Text to Beyondcited by 3
  5. 2025Deep Learning-Powered Electrical Brain Signals Analysis: Advancing Neurological Diagnosticscited by 3
  6. 2025Transformer-based EEG Decoding: A Surveycited by 3
  7. 2024Multi-Source EEG Emotion Recognition via Dynamic Contrastive Domain Adaptationcited by 2
  8. 2025Perception-Guided EEG Analysis: A Deep Learning Approach Inspired by Level of Detail (LOD) Theorycited by 1
  9. 2025Data Normalization Strategies for EEG Deep Learningcited by 1
  10. 2026MERLIN: Building Low-SNR Robust Multimodal LLMs for Electromagnetic Signalscited by 1
  11. 2026NeuroGRIP: Retrieval-Augmented Graph Refinement for Knowledge-Grounded EEG Seizure Diagnosiscited by 1
  12. 2025KnowEEG: Explainable Knowledge Driven EEG Classificationcited by 0
7 more
  1. 2025Neuro-Informed Joint Learning Enhances Cognitive Workload Decoding in Portable BCIscited by 0
  2. 2025Towards Generalizable Learning Models for EEG-Based Identification of Pain Perceptioncited by 0
  3. 2026BEAT-Net: Injecting Biomimetic Spatio-Temporal Priors for Interpretable ECG Diagnosiscited by 0
  4. 2026Rethinking Self-Training Based Cross-Subject Domain Adaptation for SSVEP Classificationcited by 0
  5. 2026Aortic Valve Disease Screening from PPG via Physiology-Guided Self-Supervised Learningcited by 0
  6. 2026Epileptic Seizure Prediction Using Patient-Adaptive Transformer Networkscited by 0
  7. 2026CoCoT-EEG: Contrastive-Pretrained Multiscale Convolutional Transformer for EEG Decodingcited by 0