mlsanityData to 14 Oct 2026

Federated Transfer Learning for EEG Signal Classification

arXiv 2004.12321 ↗First posted 26 Apr 2020cs.LG eess.SPcited by 37

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Abstract (arXiv)

The success of deep learning (DL) methods in the Brain-Computer Interfaces (BCI) field for classification of electroencephalographic (EEG) recordings has been restricted by the lack of large datasets. Privacy concerns associated with EEG signals limit the possibility of constructing a large EEG-BCI dataset by the conglomeration of multiple small ones for jointly training machine learning models. Hence, in this paper, we propose a novel privacy-preserving DL architecture named federated transfer learning (FTL) for EEG classification that is based on the federated learning framework. Working with the single-trial covariance matrix, the proposed architecture extracts common discriminative information from multi-subject EEG data with the help of domain adaptation techniques. We evaluate the performance of the proposed architecture on the PhysioNet dataset for 2-class motor imagery classification. While avoiding the actual data sharing, our FTL approach achieves 2% higher classification accuracy in a subject-adaptive analysis. Also, in the absence of multi-subject data, our architecture provides 6% better accuracy compared to other state-of-the-art DL architectures.

Key facts. Cited by 37 papers in the map (data through 14 Oct 2026). First posted on arXiv 26 Apr 2020.

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Citing papers per year: 2021: 2; 2022: 14; 2023: 6; 2024: 8; 2025: 4; 2026: 3.

37 papers.

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  1. 2022Vertical Federated Learning: Concepts, Advances and Challengescited by 108
  2. 2021HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Imagescited by 64
  3. 2022MAtt: A Manifold Attention Network for EEG Decodingcited by 44
  4. 2022A Survey on Heterogeneous Federated Learningcited by 42
  5. 2023Federated Learning for Computationally-Constrained Heterogeneous Devices: A Surveycited by 40
  6. 2022Federated Learning for Healthcare Domain - Pipeline, Applications and Challengescited by 34
  7. 2022FedMix: Mixed Supervised Federated Learning for Medical Image Segmentationcited by 23
  8. 2022IOP-FL: Inside-Outside Personalization for Federated Medical Image Segmentationcited by 22
  9. 2021Algorithm Fairness in AI for Medicine and Healthcarecited by 21
  10. 2022Tensor-CSPNet: A Novel Geometric Deep Learning Framework for Motor Imagery Classificationcited by 21
  11. 2022Graph Neural Networks on SPD Manifolds for Motor Imagery Classification: A Perspective from the Time-Frequency Analysiscited by 15
  12. 2022Domain-Invariant Representation Learning from EEG with Private Encoderscited by 12
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  1. 2024Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer Interfacescited by 7
  2. 2022Deep Optimal Transport for Domain Adaptation on SPD Manifoldscited by 6
  3. 2023EEG Decoding for Datasets with Heterogenous Electrode Configurations using Transfer Learning Graph Neural Networkscited by 6
  4. 2023Aggregating Intrinsic Information to Enhance BCI Performance through Federated Learningcited by 6
  5. 2025SPD Matrix Learning for Neuroimaging Analysis: Perspectives, Methods, and Challengescited by 6
  6. 2023Reputation-Based Federated Learning Defense to Mitigate Threats in EEG Signal Classificationcited by 5
  7. 2024Geometric Neural Network based on Phase Space for BCI-EEG decodingcited by 5
  8. 2024Minimax And Adaptive Transfer Learning for Nonparametric Classification under Distributed Differential Privacy Constraintscited by 5
  9. 2023FCA: Taming Long-tailed Federated Medical Image Classification by Classifier Anchoringcited by 4
  10. 2022Federated Transfer Learning with Multimodal Datacited by 3
  11. 2022Communication-Efficient and Privacy-Preserving Feature-based Federated Transfer Learningcited by 2
  12. 2025Siamese Network with Dual Attention for EEG-Driven Social Learning: Bridging the Human-Robot Gap in Long-Tail Autonomous Drivingcited by 2
  13. 2026SAFE: Secure and Accurate Federated Learning for Privacy-Preserving Brain-Computer Interfacescited by 2
  14. 2023A Distributed Privacy Preserving Model for the Detection of Alzheimer's Diseasecited by 1
  15. 2024Feature-based Federated Transfer Learning: Communication Efficiency, Robustness and Privacycited by 1
  16. 2024Fair Federated Data Clustering through Personalization: Bridging the Gap between Diverse Data Distributionscited by 1
  17. 2025Tailoring deep learning for real-time brain-computer interfaces: From offline models to calibration-free online decodingcited by 1
  18. 2022Federated deep transfer learning for EEG decoding using multiple BCI taskscited by 0
  19. 2022Federated Learning -- Methods, Applications and beyondcited by 0
  20. 2024CrowdTransfer: Enabling Crowd Knowledge Transfer in AIoT Communitycited by 0
  21. 2024CCVA-FL: Cross-Client Variations Adaptive Federated Learning for Medical Imagingcited by 0
  22. 2024Non-parametric regularization for class imbalance federated medical image classificationcited by 0
  23. 2025On using AI for EEG-based BCI applications: problems, current challenges and future trendscited by 0
  24. 2026Revisiting Privacy Preservation in Brain-Computer Interfaces: Conceptual Boundaries, Risk Pathways, and a Protection-Strength Grading Frameworkcited by 0
  25. 2026NEXUS-MI: Communication-Aware Federated Personalization for Gateway-Coordinated Motor-Imagery Brain-Computer Interfacescited by 0