FedEEG: Federated EEG Decoding Via inter-Subject Structure Matching

Abstract

With sufficient centralized training data coming from multiple subjects, deep learning methods have achieved powerful EEG decoding performance. However, sending each individuals’ EEG data directly to a centralized server might cause privacy leakage. To overcome this issue, we present an inter-subject structure matching-based federated EEG decoding (FedEEG) framework. First, we introduce a center loss to each client (subject), which can learn multiple virtual class centers by averaging the corresponding class-specific EEG features. To mitigate the client drift issue, we then explicitly connect the learning across multiple clients by aligning their corresponding virtual class centers, thus helping to correct the local training for individual subject. The proposed FedEEG can promote the discriminative feature learning while preventing the privacy leakage issue. The experimental results on benchmark EEG datasets show that FedEEG outperforms state-of-the-art federated learning methods.