Motor Imagery EEG Classification Based on Decision Tree Framework and Riemannian Geometry

This paper proposes a novel classification framework and a novel data reduction method to distinguish multiclass motor imagery (MI) electroencephalography (EEG) for brain computer interface (BCI) based on the manifold of covariance matrices in a Riemannian perspective. For method 1, a subject-specif...

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Detalles Bibliográficos
Autores principales: Guan, Shan, Zhao, Kai, Yang, Shuning
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2019
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6360593/
https://www.ncbi.nlm.nih.gov/pubmed/30804988
http://dx.doi.org/10.1155/2019/5627156

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6360593/
https://www.ncbi.nlm.nih.gov/pubmed/30804988
http://dx.doi.org/10.1155/2019/5627156

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