  We introduce adversarial neural networks for representation learning as a
novel approach to transfer learning in brain-computer interfaces (BCIs). The
proposed approach aims to learn subject-invariant representations by
simultaneously training a conditional variational autoencoder (cVAE) and an
adversarial network. We use shallow convolutional architectures to realize the
cVAE, and the learned encoder is transferred to extract subject-invariant
features from unseen BCI users' data for decoding. We demonstrate a
proof-of-concept of our approach based on analyses of electroencephalographic
(EEG) data recorded during a motor imagery BCI experiment.
