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.
@article{arxiv.1812.06857,
title = {Transfer Learning in Brain-Computer Interfaces with Adversarial Variational Autoencoders},
author = {Ozan Ozdenizci and Ye Wang and Toshiaki Koike-Akino and Deniz Erdogmus},
journal= {arXiv preprint arXiv:1812.06857},
year = {2018}
}
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9th International IEEE EMBS Conference on Neural Engineering (NER'19)