mlsanityData to 7 Oct 2026

Transfer Learning for EEG-Based Brain-Computer Interfaces: A Review of Progress Made Since 2016

arXiv 2004.06286 ↗First posted 13 Apr 2020cs.HC cs.LG eess.SPcited by 56

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Abstract (arXiv)

A brain-computer interface (BCI) enables a user to communicate with a computer directly using brain signals. The most common non-invasive BCI modality, electroencephalogram (EEG), is sensitive to noise/artifact and suffers between-subject/within-subject non-stationarity. Therefore, it is difficult to build a generic pattern recognition model in an EEG-based BCI system that is optimal for different subjects, during different sessions, for different devices and tasks. Usually, a calibration session is needed to collect some training data for a new subject, which is time-consuming and user unfriendly. Transfer learning (TL), which utilizes data or knowledge from similar or relevant subjects/sessions/devices/tasks to facilitate learning for a new subject/session/device/task, is frequently used to reduce the amount of calibration effort. This paper reviews journal publications on TL approaches in EEG-based BCIs in the last few years, i.e., since 2016. Six paradigms and applications -- motor imagery, event-related potentials, steady-state visual evoked potentials, affective BCIs, regression problems, and adversarial attacks -- are considered. For each paradigm/application, we group the TL approaches into cross-subject/session, cross-device, and cross-task settings and review them separately. Observations and conclusions are made at the end of the paper, which may point to future research directions.

Key facts. Cited by 56 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Apr 2020.

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Citing papers per year: 2021: 3; 2022: 7; 2023: 10; 2024: 12; 2025: 12; 2026: 12.

56 papers.

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  1. 2024Visual Decoding and Reconstruction via EEG Embeddings with Guided Diffusioncited by 79
  2. 2021Contrastive Learning of Subject-Invariant EEG Representations for Cross-Subject Emotion Recognitioncited by 58
  3. 2022SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEGcited by 53
  4. 20222021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data Setscited by 22
  5. 2022Toward cross-subject and cross-session generalization in EEG-based emotion recognition: Systematic review, taxonomy, and methodscited by 17
  6. 2024T-TIME: Test-Time Information Maximization Ensemble for Plug-and-Play BCIscited by 15
  7. 2023Calibration-free online test-time adaptation for electroencephalography motor imagery decodingcited by 12
  8. 2024SPDIM: Source-Free Unsupervised Conditional and Label Shift Adaptation in EEGcited by 12
  9. 2025DBConformer: Dual-Branch Convolutional Transformer for EEG Decodingcited by 11
  10. 2023Semi-Supervised Dual-Stream Self-Attentive Adversarial Graph Contrastive Learning for Cross-Subject EEG-based Emotion Recognitioncited by 10
  11. 2021Adversarial Attacks and Defenses in Physiological Computing: A Systematic Reviewcited by 7
  12. 2024Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer Interfacescited by 7
38 more
  1. 2025Revisiting Euclidean Alignment for Transfer Learning in EEG-Based Brain-Computer Interfacescited by 7
  2. 2025Spatial Distillation based Distribution Alignment (SDDA) for Cross-Headset EEG Classificationcited by 7
  3. 2022Transfer Learning of an Ensemble of DNNs for SSVEP BCI Spellers without User-Specific Trainingcited by 6
  4. 2022A Novel Semi-supervised Meta Learning Method for Subject-transfer Brain-computer Interfacecited by 6
  5. 2023EEG Decoding for Datasets with Heterogenous Electrode Configurations using Transfer Learning Graph Neural Networkscited by 6
  6. 2024Alignment-Based Adversarial Training (ABAT) for Improving the Robustness and Accuracy of EEG-Based BCIscited by 5
  7. 2021Boosting Template-based SSVEP Decoding by Cross-domain Transfer Learningcited by 4
  8. 2023Guiding AI-Generated Digital Content with Wireless Perceptioncited by 4
  9. 2024Review of Deep Representation Learning Techniques for Brain-Computer Interfaces and Recommendationscited by 3
  10. 2024Cross-Task Inconsistency Based Active Learning (CTIAL) for Emotion Recognitioncited by 3
  11. 2025MVCNet: Multi-View Contrastive Network for Motor Imagery Classificationcited by 3
  12. 2022Calibration-Free Driver Drowsiness Classification based on Manifold-Level Augmentationcited by 2
  13. 2024Multi-Source EEG Emotion Recognition via Dynamic Contrastive Domain Adaptationcited by 2
  14. 2024Channel Reflection: Knowledge-Driven Data Augmentation for EEG-Based Brain-Computer Interfacescited by 2
  15. 2026HEEGNet: Hyperbolic Embeddings for EEGcited by 2
  16. 2022quEEGNet: Quantum AI for Biosignal Processingcited by 1
  17. 2023Feature Reweighting for EEG-based Motor Imagery Classificationcited by 1
  18. 2023SSVEP-DAN: A Data Alignment Network for SSVEP-based Brain Computer Interfacescited by 1
  19. 2024Towards Personalized Brain-Computer Interface Application Based on Endogenous EEG Paradigmscited by 1
  20. 2024Adversarial Filtering Based Evasion and Backdoor Attacks to EEG-Based Brain-Computer Interfacescited by 1
  21. 2025SelectiveFinetuning: Enhancing Transfer Learning in Sleep Staging through Selective Domain Alignmentcited by 1
  22. 2025Unsupervised Pairwise Learning Optimization Framework for Cross-Corpus EEG-Based Emotion Recognition Based on Prototype Representationcited by 1
  23. 2023End-to-End Deep Transfer Learning for Calibration-free Motor Imagery Brain Computer Interfacescited by 0
  24. 2023Stabilizing Subject Transfer in EEG Classification with Divergence Estimationcited by 0
  25. 2023Decoding Working-Memory Load During n-Back Task Performance from High Channel NIRS Datacited by 0
  26. 2023Improving Generalization of Drowsiness State Classification by Domain-Specific Normalizationcited by 0
  27. 2024EEG classifier cross-task transfer to avoid training sessions in robot-assisted rehabilitationcited by 0
  28. 2025SSTAF: Spatial-Spectral-Temporal Attention Fusion Transformer for Motor Imagery Classificationcited by 0
  29. 2025Brain foundation model-guided source-selective domain adaptation for cross-subject EEG decodingcited by 0
  30. 2025Cross-Subject and Cross-Montage EEG Transfer Learning via Individual Tangent Space Alignment and Spatial-Riemannian Feature Fusioncited by 0
  31. 2025Toward Practical BCI: A Real-time Wireless Imagined Speech EEG Decoding Systemcited by 0
  32. 2025An energy-efficient spiking neural network with continuous learning for self-adaptive brain-machine interfacecited by 0
  33. 2025Decoding Motor Behavior Using Deep Learning and Reservoir Computingcited by 0
  34. 2026DAH-Net: A Dual-Attention Hybrid Network for Interpretable and Robust EEG-Based Emotion Recognitioncited by 0
  35. 2026Interpretable Fuzzy Modeling Reveals Population-Level Representation Differences in P300 Brain Computer Interfaces Across Neurodivergent and Neurotypical Cohortscited by 0
  36. 2026EVA-Net: Subject-Independent EEG Motor Decoding with Video-Derived Motor Priorscited by 0
  37. 2026ERP-XTTN: Interpretable Prototype-Guided Cross-Attention for Cross-Subject ERP Classificationcited by 0
  38. 2026Masked Generative-Contrastive Representation Learning for Cross-Dataset EEG-Based Emotion Recognitioncited by 0

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