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

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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Bibliographic Details
Main Authors: Juan Lorenzo Hagad, Tsukasa Kimura, Ken-ichi Fukui, Masayuki Numao
Format: Article
Language:English
Published: MDPI AG 2021-03-01
Series:Sensors
Subjects:
electroencephalography
emotion modeling
subject independence
domain generalization
variational autoencoder
domain adversarial network
Online Access:https://www.mdpi.com/1424-8220/21/5/1792

Internet

https://www.mdpi.com/1424-8220/21/5/1792

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