Exploiting Federated Learning for EEG-based Brain-Computer Interface System

Motor imagery (MI) is a widely used technique in brain-computer interface (BCI) systems, which allows users to control external devices using their brain signals. Electroen-cephalogram (EEG) signals are commonly used to detect and classify MI tasks. However, the lack of annotated data hampers the performance of machine learning (ML) algorithms. Federated learning (FL) is a promising approach to address these challenges, as it allows models to be trained on decentralized datasets without exchanging data. This study proposes an FL approach for MI-EEG signal classification using a convolutional neural network (CNN) on the PhysioNet dataset containing EEG recordings of left and right-hand imagery movements. We evaluate the performance of the FL approach using two different aggregation methods, namely FedAvg and FedProx, and compare it to the centralized ML approach. Furthermore, we explore the effect of increasing the number of clients who participate in the learning process on the performance of the model. Our findings demonstrate that FL maintains consistent classification accuracy comparable to the centralized ML approach while reducing data leakage risks. Thus, FL shows great promise as an essential instrument for MI-EEG signal classification and BCI systems, enabling distributed training of a comprehensive model when privacy-sensitive data is dispersed across multiple clients.