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Deep learning has been successful in BCI decoding. However, it is verydata-hungry and requires pooling data from multiple sources. EEG data fromvarious sources decrease the decoding performance due to negative transfer.Recently, transfer learning for EEG decoding has been suggested as a remedy andbecome subject to recent BCI competitions (e.g. BEETL), but there are twocomplications in combining data from many subjects. First, privacy is notprotected as highly personal brain data needs to be shared (and copied acrossincreasingly tight information governance boundaries). Moreover, BCI data arecollected from different sources and are often based on different BCI tasks,which has been thought to limit their reusability. Here, we demonstrate afederated deep transfer learning technique, the Multi-dataset FederatedSeparate-Common-Separate Network (MF-SCSN) based on our previous work of SCSN,which integrates privacy-preserving properties into deep transfer learning toutilise data sets with different tasks. This framework trains a BCI decoderusing different source data sets obtained from different imagery tasks (e.g.some data sets with hands and feet, vs others with single hands and tongue,etc). Therefore, by introducing privacy-preserving transfer learningtechniques, we unlock the reusability and scalability of existing BCI datasets. We evaluated our federated transfer learning method on the NeurIPS 2021BEETL competition BCI task. The proposed architecture outperformed the baselinedecoder by 3%. Moreover, compared with the baseline and other transfer learningalgorithms, our method protects the privacy of the brain data from differentdata centres.

Federated deep transfer learning for EEG decoding using multiple BCI tasks