A Fused Multidimensional EEG Classification Method Based on an Extreme Tree Feature Selection

When a brain-computer interface (BCI) is designed, high classification accuracy is difficult to obtain for motor imagery (MI) electroencephalogram (EEG) signals in view of their relatively low signal-to-noise ratio. In this paper, a fused multidimensional classification method based on extreme tree...

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Detalles Bibliográficos
Autores principales: Lin, Ruijing, Dong, Chaoyi, Ma, Pengfei, Ma, Shuang, Chen, Xiaoyan, Liu, Huanzi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9377856/
https://www.ncbi.nlm.nih.gov/pubmed/35978888
http://dx.doi.org/10.1155/2022/7609196

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9377856/
https://www.ncbi.nlm.nih.gov/pubmed/35978888
http://dx.doi.org/10.1155/2022/7609196

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