Federated Transfer Learning for EEG Signal Classification

• Published in: Proceedings of the annual international conference of the IEEE Engineering in Medicine and Biology Society pp. 3040 - 3045
• Main Authors: Ju, Ce, Gao, Dashan, Mane, Ravikiran, Tan, Ben, Liu, Yang, Guan, Cuntai
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2020
• ISSN: 1558-4615
• DOI: 10.1109/EMBC44109.2020.9175344

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.