Transfer Learning in Brain-Computer Interfaces with Adversarial Variational Autoencoders

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 adve...

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Vydáno v:	International IEEE/EMBS Conference on Neural Engineering (Online) s. 207 - 210
Hlavní autoři:	Ozdenizci, Ozan, Wang, Ye, Koike-Akino, Toshiaki, Erdogmus, Deniz
Médium:	Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	IEEE 01.03.2019
Témata:	adversarial networks Brain modeling Brain-computer interfaces convolutional neural networks Decoding EEG Electroencephalography Feature extraction representation learning Training Training data transfer learning variational autoencoders
ISSN:	1948-3554
On-line přístup:	Získat plný text
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Popis
Shrnutí:	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.
ISSN:	1948-3554
DOI:	10.1109/NER.2019.8716897