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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...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Hindawi
2019
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| Materias: | |
| 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 |