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Transfer Learning in Brain-Computer Interfaces

arXiv 1512.00296 ↗First posted 1 Dec 2015cs.HC q-bio.NCcited by 39

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Abstract (arXiv)

The performance of brain-computer interfaces (BCIs) improves with the amount of available training data, the statistical distribution of this data, however, varies across subjects as well as across sessions within individual subjects, limiting the transferability of training data or trained models between them. In this article, we review current transfer learning techniques in BCIs that exploit shared structure between training data of multiple subjects and/or sessions to increase performance. We then present a framework for transfer learning in the context of BCIs that can be applied to any arbitrary feature space, as well as a novel regression estimation method that is specifically designed for the structure of a system based on the electroencephalogram (EEG). We demonstrate the utility of our framework and method on subject-to-subject transfer in a motor-imagery paradigm as well as on session-to-session transfer in one patient diagnosed with amyotrophic lateral sclerosis (ALS), showing that it is able to outperform other comparable methods on an identical dataset.

Key facts. Cited by 39 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Dec 2015.

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Citing papers per year: 2021: 3; 2022: 5; 2023: 5; 2024: 7; 2025: 8; 2026: 11.

39 papers.

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  1. 2021Contrastive Learning of Subject-Invariant EEG Representations for Cross-Subject Emotion Recognitioncited by 58
  2. 2022SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEGcited by 53
  3. 20222021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data Setscited by 22
  4. 2021Inter-subject Deep Transfer Learning for Motor Imagery EEG Decodingcited by 12
  5. 2023EEGMatch: Learning with Incomplete Labels for Semi-Supervised EEG-based Cross-Subject Emotion Recognitioncited by 10
  6. 2024Unveiling Thoughts: A Review of Advancements in EEG Brain Signal Decoding into Textcited by 10
  7. 2023Evaluating the structure of cognitive tasks with transfer learningcited by 8
  8. 2021Model-Agnostic Meta-Learning for EEG Motor Imagery Decoding in Brain-Computer-Interfacingcited by 4
  9. 2022Introducing Block-Toeplitz Covariance Matrices to Remaster Linear Discriminant Analysis for Event-related Potential Brain-computer Interfacescited by 4
  10. 2022Towards physiology-informed data augmentation for EEG-based BCIscited by 4
  11. 2023UMM: Unsupervised Mean-difference Maximizationcited by 4
  12. 2023Transfer Learning between Motor Imagery Datasets using Deep Learning -- Validation of Framework and Comparison of Datasetscited by 4
27 more
  1. 2025Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generationcited by 4
  2. 2024M3D: Manifold-based Domain Adaptation with Dynamic Distribution for Non-Deep Transfer Learning in Cross-subject and Cross-session EEG-based Emotion Recognitioncited by 3
  3. 2024Review of Deep Representation Learning Techniques for Brain-Computer Interfaces and Recommendationscited by 3
  4. 2026A Negative-Control Protocol for Clinical EEG Foundation-Model Benchmarks: Dataset Identity and External-Cohort Stress Testingcited by 3
  5. 2024Weakly supervised covariance matrices alignment through Stiefel matrices estimation for MEG applicationscited by 2
  6. 2024PL-DCP: A Pairwise Learning framework with Domain and Class Prototypes for EEG emotion recognition under unseen target conditionscited by 2
  7. 2024iFuzzyTL: Interpretable Fuzzy Transfer Learning for SSVEP BCI Systemcited by 1
  8. 2025Unsupervised Pairwise Learning Optimization Framework for Cross-Corpus EEG-Based Emotion Recognition Based on Prototype Representationcited by 1
  9. 2022Federated deep transfer learning for EEG decoding using multiple BCI taskscited by 0
  10. 2023Stabilizing Subject Transfer in EEG Classification with Divergence Estimationcited by 0
  11. 2024Evaluating Fast Adaptability of Neural Networks for Brain-Computer Interfacecited by 0
  12. 2025QSVM-QNN: Quantum Support Vector Machine Based Quantum Neural Network Learning Algorithm for Brain-Computer Interfacing Systemscited by 0
  13. 2025System Filter-Based Common Components Modeling for Cross-Subject EEG Decodingcited by 0
  14. 2025Adaptive Split-MMD Training for Small-Sample Cross-Dataset P300 EEG Classificationcited by 0
  15. 2025Geometry-Aware Deep Congruence Networks for Manifold Learning in Cross-Subject Motor Imagerycited by 0
  16. 2025Gated Uncertainty-Aware Runtime Dual Invariants for Neural Signal-Controlled Roboticscited by 0
  17. 2025EEG-based Graph-guided Domain Adaptation for Robust Cross-Session Emotion Recognitioncited by 0
  18. 2026Test-Time Adaptation for EEG Foundation Models: A Systematic Study under Real-World Distribution Shiftscited by 0
  19. 2026Average Rankings Mask Per-Subject Optimality: A Friedman-Nemenyi Benchmark of EEG Motor-Imagery BCI Decoderscited by 0
  20. 2026Itextsuperscript2RiMA: Spectral Riemannian Representation with Temporal Attention for Mental Stress Detection based on EEG Signalscited by 0
  21. 2026Masked Generative-Contrastive Representation Learning for Cross-Dataset EEG-Based Emotion Recognitioncited by 0
  22. 2026Universal BCI Personalization: One API for Frozen EEG Trunks and Foundation Modelscited by 0
  23. 2026Joint Text-Audio Alignment for EEG-to-Text Decoding in Chinese Speech Production and Perceptioncited by 0
  24. 2026Frequency-Decorrelated Temporal Ensembles for EEG--fNIRS Imagined-Handwriting Decodingcited by 0
  25. 2026Rectifying Geometric Misalignment: Online Source-Free Adaptation for Class-Imbalanced EEGcited by 0
  26. 2026Behavioral Latency as Weak Event-Time Supervision for EEG Reaction-Time Decodingcited by 0
  27. 2026EEG-VID: Task-Guided Latent Predictive Pretraining for EEG Decoding and Assistive Target Selectioncited by 0