  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.
