  Building subject-independent deep learning models for EEG decoding faces the
challenge of strong covariate-shift across different datasets, subjects and
recording sessions. Our approach to address this difficulty is to explicitly
align feature distributions at various layers of the deep learning model, using
both simple statistical techniques as well as trainable methods with more
representational capacity. This follows in a similar vein as covariance-based
alignment methods, often used in a Riemannian manifold context. The methodology
proposed herein won first place in the 2021 Benchmarks in EEG Transfer Learning
(BEETL) competition, hosted at the NeurIPS conference. The first task of the
competition consisted of sleep stage classification, which required the
transfer of models trained on younger subjects to perform inference on multiple
subjects of older age groups without personalized calibration data, requiring
subject-independent models. The second task required to transfer models trained
on the subjects of one or more source motor imagery datasets to perform
inference on two target datasets, providing a small set of personalized
calibration data for multiple test subjects.
