mlsanityData to 7 Oct 2026

Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer Interfaces

arXiv 2412.01079 ↗First posted 2 Dec 2024cs.LG cs.HCcited by 7

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Abstract (arXiv)

Training an accurate classifier for EEG-based brain-computer interface (BCI) requires EEG data from a large number of users, whereas protecting their data privacy is a critical consideration. Federated learning (FL) is a promising solution to this challenge. This paper proposes Federated classification with local Batch-specific batch normalization and Sharpness-aware minimization (FedBS) for privacy protection in EEG-based motor imagery (MI) classification. FedBS utilizes local batch-specific batch normalization to reduce data discrepancies among different clients, and sharpness-aware minimization optimizer in local training to improve model generalization. Experiments on three public MI datasets using three popular deep learning models demonstrated that FedBS outperformed six state-of-the-art FL approaches. Remarkably, it also outperformed centralized training, which does not consider privacy protection at all. In summary, FedBS protects user EEG data privacy, enabling multiple BCI users to participate in large-scale machine learning model training, which in turn improves the BCI decoding accuracy.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 25 of its 50 references that are in the map. First posted on arXiv 2 Dec 2024.

Builds on

25 of 50 references.

References · 25

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  4. 2020Sharpness-Aware Minimization for Efficiently Improving Generalizationcited by 1,404
  5. 2020Adaptive Federated Optimizationcited by 1,036
  6. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 836
  7. 2021Model-Contrastive Federated Learningcited by 762
  8. 2016EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfacescited by 616
  9. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 565
  10. 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 371
  11. 2020Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfercited by 173
  12. 2022Generalized Federated Learning via Sharpness Aware Minimizationcited by 144
13 more
  1. 2022Improving Generalization in Federated Learning by Seeking Flat Minimacited by 98
  2. 2018MOABB: Trustworthy algorithm benchmarking for BCIscited by 80
  3. 2018Privacy Preserving Machine Learning: Threats and Solutionscited by 78
  4. 2018Transfer Learning for Brain-Computer Interfaces: A Euclidean Space Data Alignment Approachcited by 76
  5. 2021Privacy-Preserving Machine Learning: Methods, Challenges and Directionscited by 58
  6. 2020Transfer Learning for EEG-Based Brain-Computer Interfaces: A Review of Progress Made Since 2016cited by 56
  7. 2020Federated Transfer Learning for EEG Signal Classificationcited by 36
  8. 2020Transfer Learning for Motor Imagery Based Brain-Computer Interfaces: A Complete Pipelinecited by 31
  9. 2018How deep is deep enough? -- Quantifying class separability in the hidden layers of deep neural networkscited by 28
  10. 2022Accelerated Federated Learning with Decoupled Adaptive Optimizationcited by 23
  11. 2023FedFA: Federated Feature Augmentationcited by 22
  12. 2019Protecting Privacy of Users in Brain-Computer Interface Applicationscited by 13
  13. 2023Aggregating Intrinsic Information to Enhance BCI Performance through Federated Learningcited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 3.

7 papers.

  1. 2025Revisiting Euclidean Alignment for Transfer Learning in EEG-Based Brain-Computer Interfacescited by 7
  2. 2024PAT: Privacy-Preserving Adversarial Transfer for Accurate, Robust and Privacy-Preserving EEG Decodingcited by 2
  3. 2026SAFE: Secure and Accurate Federated Learning for Privacy-Preserving Brain-Computer Interfacescited by 1
  4. 2025On using AI for EEG-based BCI applications: problems, current challenges and future trendscited by 0
  5. 2025Federated Learning in Offline and Online EMG Decoding: A Privacy and Performance Perspectivecited by 0
  6. 2026NEXUS-MI: Communication-Aware Federated Personalization for Gateway-Coordinated Motor-Imagery Brain-Computer Interfacescited by 0
  7. 2026Personalised federated learning for Riemannian and Euclidean EEG decodingcited by 0