EEG Feature Selection via Stacked Deep Embedded Regression With Joint Sparsity

In the field of brain-computer interface (BCI), selecting efficient and robust features is very seductive for artificial intelligence (AI)-assisted clinical diagnosis. In this study, based on an embedded feature selection model, we construct a stacked deep structure for feature selection in a layer-...

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Detalles Bibliográficos
Autores principales: Jiang, Kui, Tang, Jiaxi, Wang, Yulong, Qiu, Chengyu, Zhang, Yuanpeng, Lin, Chuang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Neuroscience
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7423875/
https://www.ncbi.nlm.nih.gov/pubmed/32848581
http://dx.doi.org/10.3389/fnins.2020.00829

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7423875/
https://www.ncbi.nlm.nih.gov/pubmed/32848581
http://dx.doi.org/10.3389/fnins.2020.00829

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