Learning Subject-Generalized Topographical EEG Embeddings Using Deep Variational Autoencoders and Domain-Adversarial Regularization

Two of the biggest challenges in building models for detecting emotions from electroencephalography (EEG) devices are the relatively small amount of labeled samples and the strong variability of signal feature distributions between different subjects. In this study, we propose a context-generalized...

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Auteurs principaux:	Juan Lorenzo Hagad, Tsukasa Kimura, Ken-ichi Fukui, Masayuki Numao
Format:	Artigo
Langue:	Inglês
Publié:	MDPI AG 2021-03-01
Collection:	Sensors
Sujets:	electroencephalography emotion modeling subject independence domain generalization variational autoencoder domain adversarial network
Accès en ligne:	https://www.mdpi.com/1424-8220/21/5/1792
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