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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...
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| Publicado no: | Comput Intell Neurosci |
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| Main Authors: | , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Hindawi
2019
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6360593/ https://ncbi.nlm.nih.gov/pubmed/30804988 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1155/2019/5627156 |
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