Cargando…
A Multifrequency Brain Network-Based Deep Learning Framework for Motor Imagery Decoding
Motor imagery (MI) is an important part of brain-computer interface (BCI) research, which could decode the subject's intention and help remodel the neural system of stroke patients. Therefore, accurate decoding of electroencephalography- (EEG-) based motion imagination has received a lot of att...
Autores principales: | Xue, Juntao, Ren, Feiyue, Sun, Xinlin, Yin, Miaomiao, Wu, Jialing, Ma, Chao, Gao, Zhongke |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7787825/ https://www.ncbi.nlm.nih.gov/pubmed/33505456 http://dx.doi.org/10.1155/2020/8863223 |
Ejemplares similares
-
Deep Learning Based Inter-subject Continuous Decoding of Motor Imagery for Practical Brain-Computer Interfaces
por: Roy, Sujit, et al.
Publicado: (2020) -
Motor imagery of hand actions: Decoding the content of motor imagery from brain activity in frontal and parietal motor areas
por: Pilgramm, Sebastian, et al.
Publicado: (2015) -
An Optimized Channel Selection Method Based on Multifrequency CSP-Rank for Motor Imagery-Based BCI System
por: Feng, Jian Kui, et al.
Publicado: (2019) -
Multi-Class Motor Imagery EEG Decoding for Brain-Computer Interfaces
por: Wang, Deng, et al.
Publicado: (2012) -
A Decoding Scheme for Incomplete Motor Imagery EEG With Deep Belief Network
por: Chu, Yaqi, et al.
Publicado: (2018)