  Deep learning has been successful in BCI decoding. However, it is very
data-hungry and requires pooling data from multiple sources. EEG data from
various sources decrease the decoding performance due to negative transfer.
Recently, transfer learning for EEG decoding has been suggested as a remedy and
become subject to recent BCI competitions (e.g. BEETL), but there are two
complications in combining data from many subjects. First, privacy is not
protected as highly personal brain data needs to be shared (and copied across
increasingly tight information governance boundaries). Moreover, BCI data are
collected from different sources and are often based on different BCI tasks,
which has been thought to limit their reusability. Here, we demonstrate a
federated deep transfer learning technique, the Multi-dataset Federated
Separate-Common-Separate Network (MF-SCSN) based on our previous work of SCSN,
which integrates privacy-preserving properties into deep transfer learning to
utilise data sets with different tasks. This framework trains a BCI decoder
using 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 learning
techniques, we unlock the reusability and scalability of existing BCI data
sets. We evaluated our federated transfer learning method on the NeurIPS 2021
BEETL competition BCI task. The proposed architecture outperformed the baseline
decoder by 3%. Moreover, compared with the baseline and other transfer learning
algorithms, our method protects the privacy of the brain data from different
data centres.
