  Machine learning has enhanced the performance of decoding signals indicating
human behaviour. EEG decoding, as an exemplar indicating neural activity and
human thoughts non-invasively, has been helpful in neural activity analysis and
aiding patients via brain-computer interfaces. However, training machine
learning algorithms on EEG encounters two primary challenges: variability
across data sets and privacy concerns using data from individuals and data
centres. Our objective is to address these challenges by integrating transfer
learning for data variability and federated learning for data privacy into a
unified approach. We introduce the Sandwich as a novel deep privacy-preserving
meta-framework combining transfer learning and federated learning. The Sandwich
framework comprises three components: federated networks (first layers) that
handle data set differences at the input level, a shared network (middle layer)
learning common rules and applying transfer learning, and individual
classifiers (final layers) for specific tasks of each data set. It enables the
central network (central server) to benefit from multiple data sets, while
local branches (local servers) maintain data and label privacy. We evaluated
the `Sandwich' meta-architecture in various configurations using the BEETL
motor imagery challenge, a benchmark for heterogeneous EEG data sets. Compared
with baseline models, our `Sandwich' implementations showed superior
performance. The best-performing model, the Inception Sandwich with deep set
alignment (Inception-SD-Deepset), exceeded baseline methods by 9%. The
`Sandwich' framework demonstrates significant advancements in federated deep
transfer learning for diverse tasks and data sets. It outperforms conventional
deep learning methods, showcasing the potential for effective use of larger,
heterogeneous data sets with enhanced privacy as a model-agnostic
meta-framework.
