Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer Interfaces
This addresses privacy concerns in brain-computer interfaces for users, though it is incremental as it builds on existing federated learning techniques.
The paper tackled the challenge of training accurate EEG-based motor imagery classifiers while preserving user data privacy by proposing FedBS, a federated learning method that outperformed six state-of-the-art FL approaches and even centralized training on three public datasets.
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.