Identification of Anisomerous Motor Imagery EEG Signals Based on Complex Algorithms

Motor imagery (MI) electroencephalograph (EEG) signals are widely applied in brain-computer interface (BCI). However, classified MI states are limited, and their classification accuracy rates are low because of the characteristics of nonlinearity and nonstationarity. This study proposes a novel MI p...

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Detalles Bibliográficos
Autores principales: Liu, Rensong, Zhang, Zhiwen, Duan, Feng, Zhou, Xin, Meng, Zixuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2017
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5569879/
https://www.ncbi.nlm.nih.gov/pubmed/28874909
http://dx.doi.org/10.1155/2017/2727856

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5569879/
https://www.ncbi.nlm.nih.gov/pubmed/28874909
http://dx.doi.org/10.1155/2017/2727856

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