Cargando…
Classification of Motor Imagery Electroencephalography Signals Based on Image Processing Method
In recent years, more and more frameworks have been applied to brain-computer interface technology, and electroencephalogram-based motor imagery (MI-EEG) is developing rapidly. However, it is still a challenge to improve the accuracy of MI-EEG classification. A deep learning framework termed IS-CBAM...
Autores principales: | Chen, Zhongye, Wang, Yijun, Song, Zhongyan |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8309641/ https://www.ncbi.nlm.nih.gov/pubmed/34300386 http://dx.doi.org/10.3390/s21144646 |
Ejemplares similares
-
Efficient Classification of Motor Imagery Electroencephalography Signals Using Deep Learning Methods
por: Majidov, Ikhtiyor, et al.
Publicado: (2019) -
Electroencephalography-Based Brain–Computer Interface Motor Imagery Classification
por: Mohammadi, Ehsan, et al.
Publicado: (2021) -
Classification of Four-Class Motor Imagery Employing Single-Channel Electroencephalography
por: Ge, Sheng, et al.
Publicado: (2014) -
Electroencephalography Signal Analysis for Human Activities Classification: A Solution Based on Machine Learning and Motor Imagery
por: de Brito Guerra, Tarciana C., et al.
Publicado: (2023) -
An Intelligent Motor Imagery Detection System Using Electroencephalography with Adaptive Wavelets
por: Khare, Smith K., et al.
Publicado: (2022)