Cargando…
Exploring the Role of Visual Guidance in Motor Imagery-Based Brain-Computer Interface: An EEG Microstate-Specific Functional Connectivity Study
Motor imagery-based brain–computer interfaces (BCI) have been widely recognized as beneficial tools for rehabilitation applications. Moreover, visually guided motor imagery was introduced to improve the rehabilitation impact. However, the reported results to support these techniques remain unsatisfa...
Autores principales: | Wang, Tianjun, Chen, Yun-Hsuan, Sawan, Mohamad |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10044873/ https://www.ncbi.nlm.nih.gov/pubmed/36978672 http://dx.doi.org/10.3390/bioengineering10030281 |
Ejemplares similares
-
Predicting Motor Imagery BCI Performance Based on EEG Microstate Analysis
por: Cui, Yujie, et al.
Publicado: (2023) -
EEG datasets for motor imagery brain–computer interface
por: Cho, Hohyun, et al.
Publicado: (2017) -
Intelligent Classification Technique of Hand Motor Imagery Using EEG Beta Rebound Follow-Up Pattern
por: Wang, Jiachen, et al.
Publicado: (2022) -
Motor Imagery Classification Based on EEG Sensing with Visual and Vibrotactile Guidance
por: Batistić, Luka, et al.
Publicado: (2023) -
Multi-Class Motor Imagery EEG Decoding for Brain-Computer Interfaces
por: Wang, Deng, et al.
Publicado: (2012)