mlsanityData to 14 Oct 2026

Cross-Subject Generalization for EEG Decoding: A Survey of Deep Learning Methods

arXiv 2604.27033 ↗First posted 29 Apr 2026cs.LG eess.SPcited by 3

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Abstract (arXiv)

Deep learning for cross-subject EEG decoding is hindered by high inter-subject variability, which introduces a severe domain shift between training and unseen test subjects. This survey presents a comprehensive review of deep learning methodologies specifically engineered to address this cross-subject generalization challenge. To ground this analysis, we formalize the cross-subject setting as a multi-source domain problem and delineate the rigorous, subject-independent evaluation protocols required for valid assessment. Central to this survey is a systematic taxonomy of the current literature into discrete methodological families, including feature alignment, adversarial learning, feature disentanglement, and contrastive learning. We conclude by examining three critical elements for advancing robust, real-world decoding: the theoretical limitations of current methodologies, the structural value of subject identity, and the emergence of EEG foundation models.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 30 of its 95 references that are in the map. First posted on arXiv 29 Apr 2026.

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30 of 95 references.

References · 30

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  8. 2016EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfacescited by 628
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  11. 2024CBraMod: A Criss-Cross Brain Foundation Model for EEG Decodingcited by 199
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18 more
  1. 2025REVE: A Foundation Model for EEG -- Adapting to Any Setup with Large-Scale Pretraining on 25,000 Subjectscited by 80
  2. 2021Contrastive Learning of Subject-Invariant EEG Representations for Cross-Subject Emotion Recognitioncited by 60
  3. 2023Contrast Everything: A Hierarchical Contrastive Framework for Medical Time-Seriescited by 59
  4. 2022SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEGcited by 53
  5. 2024Medformer: A Multi-Granularity Patching Transformer for Medical Time-Series Classificationcited by 53
  6. 2020Epileptic Seizures Detection Using Deep Learning Techniques: A Reviewcited by 32
  7. 2023ManyDG: Many-domain Generalization for Healthcare Applicationscited by 23
  8. 2022Toward cross-subject and cross-session generalization in EEG-based emotion recognition: Systematic review, taxonomy, and methodscited by 17
  9. 2024Neural decoding from stereotactic EEG: accounting for electrode variability across subjectscited by 17
  10. 2019Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure Detectioncited by 14
  11. 2025EEG Foundation Challenge: From Cross-Task to Cross-Subject EEG Decodingcited by 12
  12. 2023EEG-DG: A Multi-Source Domain Generalization Framework for Motor Imagery EEG Classificationcited by 9
  13. 2025BrainOOD: Out-of-distribution Generalizable Brain Network Analysiscited by 7
  14. 2024Repurposing Foundation Model for Generalizable Medical Time Series Classificationcited by 6
  15. 2024Graph Adapter of EEG Foundation Models for Parameter Efficient Fine Tuningcited by 5
  16. 2025mixEEG: Enhancing EEG Federated Learning for Cross-subject EEG Classification with Tailored mixupcited by 5
  17. 2024The Sandwich meta-framework for architecture agnostic deep privacy-preserving transfer learning for non-invasive brainwave decodingcited by 3
  18. 2025FAPEX: Fractional Amplitude-Phase Expressor for Robust Cross-Subject Seizure Predictioncited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Learning Generalizable Reconstruction of High-Dimensional Neural Dynamicscited by 0
  2. 2026Adaptive Forgetting for Nonstationary Optimization: Towards Robust EEG Decodingcited by 0
  3. 2026Structured Visual Target Learning For Cross-Subject eeg-to-image retrievalcited by 0