# Federated Transfer Learning for EEG Signal Classification

arXiv: https://arxiv.org/abs/2004.12321 · HTML: https://mlsanity.com/paper/2004.12321/

## Key facts

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

## Built on by (most cited first, 30 of 37)

- [Vertical Federated Learning: Concepts, Advances and Challenges](https://mlsanity.com/paper/2211.12814/) (2022)
- [HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Images](https://mlsanity.com/paper/2112.10775/) (2021)
- [MAtt: A Manifold Attention Network for EEG Decoding](https://mlsanity.com/paper/2210.01986/) (2022)
- [A Survey on Heterogeneous Federated Learning](https://mlsanity.com/paper/2210.04505/) (2022)
- [Federated Learning for Computationally-Constrained Heterogeneous Devices: A Survey](https://mlsanity.com/paper/2307.09182/) (2023)
- [Federated Learning for Healthcare Domain - Pipeline, Applications and Challenges](https://mlsanity.com/paper/2211.07893/) (2022)
- [FedMix: Mixed Supervised Federated Learning for Medical Image Segmentation](https://mlsanity.com/paper/2205.01840/) (2022)
- [IOP-FL: Inside-Outside Personalization for Federated Medical Image Segmentation](https://mlsanity.com/paper/2204.08467/) (2022)
- [Algorithm Fairness in AI for Medicine and Healthcare](https://mlsanity.com/paper/2110.00603/) (2021)
- [Tensor-CSPNet: A Novel Geometric Deep Learning Framework for Motor Imagery Classification](https://mlsanity.com/paper/2202.02472/) (2022)
- [Graph Neural Networks on SPD Manifolds for Motor Imagery Classification: A Perspective from the Time-Frequency Analysis](https://mlsanity.com/paper/2211.02641/) (2022)
- [Domain-Invariant Representation Learning from EEG with Private Encoders](https://mlsanity.com/paper/2201.11613/) (2022)
- [Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer Interfaces](https://mlsanity.com/paper/2412.01079/) (2024)
- [Deep Optimal Transport for Domain Adaptation on SPD Manifolds](https://mlsanity.com/paper/2201.05745/) (2022)
- [EEG Decoding for Datasets with Heterogenous Electrode Configurations using Transfer Learning Graph Neural Networks](https://mlsanity.com/paper/2306.13109/) (2023)
- [Aggregating Intrinsic Information to Enhance BCI Performance through Federated Learning](https://mlsanity.com/paper/2308.11636/) (2023)
- [SPD Matrix Learning for Neuroimaging Analysis: Perspectives, Methods, and Challenges](https://mlsanity.com/paper/2504.18882/) (2025)
- [Reputation-Based Federated Learning Defense to Mitigate Threats in EEG Signal Classification](https://mlsanity.com/paper/2401.01896/) (2023)
- [Geometric Neural Network based on Phase Space for BCI-EEG decoding](https://mlsanity.com/paper/2403.05645/) (2024)
- [Minimax And Adaptive Transfer Learning for Nonparametric Classification under Distributed Differential Privacy Constraints](https://mlsanity.com/paper/2406.20088/) (2024)
- [FCA: Taming Long-tailed Federated Medical Image Classification by Classifier Anchoring](https://mlsanity.com/paper/2305.00738/) (2023)
- [Federated Transfer Learning with Multimodal Data](https://mlsanity.com/paper/2209.03137/) (2022)
- [Communication-Efficient and Privacy-Preserving Feature-based Federated Transfer Learning](https://mlsanity.com/paper/2209.05395/) (2022)
- [Siamese Network with Dual Attention for EEG-Driven Social Learning: Bridging the Human-Robot Gap in Long-Tail Autonomous Driving](https://mlsanity.com/paper/2504.10296/) (2025)
- [SAFE: Secure and Accurate Federated Learning for Privacy-Preserving Brain-Computer Interfaces](https://mlsanity.com/paper/2601.05789/) (2026)
- [A Distributed Privacy Preserving Model for the Detection of Alzheimer's Disease](https://mlsanity.com/paper/2312.10237/) (2023)
- [Feature-based Federated Transfer Learning: Communication Efficiency, Robustness and Privacy](https://mlsanity.com/paper/2405.09014/) (2024)
- [Fair Federated Data Clustering through Personalization: Bridging the Gap between Diverse Data Distributions](https://mlsanity.com/paper/2407.04302/) (2024)
- [Tailoring deep learning for real-time brain-computer interfaces: From offline models to calibration-free online decoding](https://mlsanity.com/paper/2507.06779/) (2025)
- [Federated deep transfer learning for EEG decoding using multiple BCI tasks](https://mlsanity.com/paper/2211.10976/) (2022)

## 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.