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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Veröffentlicht in:	International IEEE/EMBS Conference on Neural Engineering (Online) S. 207 - 210
Hauptverfasser:	Ozdenizci, Ozan, Wang, Ye, Koike-Akino, Toshiaki, Erdogmus, Deniz
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.03.2019
Schlagworte:	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
Online-Zugang:	Volltext
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