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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Detalles Bibliográficos
Autores principales: Hagad, Juan Lorenzo, Kimura, Tsukasa, Fukui, Ken-ichi, Numao, Masayuki
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7961341/
https://www.ncbi.nlm.nih.gov/pubmed/33806712
http://dx.doi.org/10.3390/s21051792

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7961341/
https://www.ncbi.nlm.nih.gov/pubmed/33806712
http://dx.doi.org/10.3390/s21051792

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