Cargando…
EEG channel selection based on sequential backward floating search for motor imagery classification
Brain-computer interfaces (BCIs) based on motor imagery (MI) utilizing multi-channel electroencephalogram (EEG) data are commonly used to improve motor function of people with motor disabilities. EEG channel selection can enhance MI classification accuracy by selecting informative channels, accordin...
Autores principales: | Tang, Chao, Gao, Tianyi, Li, Yuanhao, Chen, Badong |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9633952/ https://www.ncbi.nlm.nih.gov/pubmed/36340754 http://dx.doi.org/10.3389/fnins.2022.1045851 |
Ejemplares similares
-
EEG oscillatory patterns and classification of sequential compound limb motor imagery
por: Yi, Weibo, et al.
Publicado: (2016) -
Data Augmentation: Using Channel-Level Recombination to Improve Classification Performance for Motor Imagery EEG
por: Pei, Yu, et al.
Publicado: (2021) -
Local and global convolutional transformer-based motor imagery EEG classification
por: Zhang, Jiayang, et al.
Publicado: (2023) -
Early-stage fusion of EEG and fNIRS improves classification of motor imagery
por: Li, Yang, et al.
Publicado: (2023) -
A Generalizable and Discriminative Learning Method for Deep EEG-Based Motor Imagery Classification
por: Huang, Xiuyu, et al.
Publicado: (2021)