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

TL;DR

This paper introduces FedBS, a federated learning method for EEG-based motor imagery classification that enhances privacy and model accuracy by using local batch normalization and sharpness-aware minimization.

Contribution

It proposes FedBS, a novel federated learning approach combining local batch normalization and sharpness-aware minimization for privacy-preserving EEG classification.

Findings

FedBS outperforms six state-of-the-art FL methods.

FedBS surpasses centralized training in accuracy.

FedBS effectively protects user EEG data privacy.

Abstract

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.