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arXiv · 1812.06857

Transfer Learning in Brain-Computer Interfaces with Adversarial Variational Autoencoders

Ozan Ozdenizci·Ye Wang·Toshiaki Koike-Akino·Deniz Erdogmus
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Abstract

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.

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Ozan Ozdenizci, Ye Wang, Toshiaki Koike-Akino, Deniz Erdogmus. 2018-12-17. Transfer Learning in Brain-Computer Interfaces with Adversarial Variational Autoencoders. https://arxiv.org/abs/1812.06857

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