The 'Sandwich' meta-framework for architecture agnostic deep privacy-preserving transfer learning for non-invasive brainwave decoding

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.