Cargando…
A sequential learning model with GNN for EEG-EMG-based stroke rehabilitation BCI
INTRODUCTION: Brain-computer interfaces (BCIs) have the potential in providing neurofeedback for stroke patients to improve motor rehabilitation. However, current BCIs often only detect general motor intentions and lack the precise information needed for complex movement execution, mainly due to ins...
Autores principales: | Li, Haoyang, Ji, Hongfei, Yu, Jian, Li, Jie, Jin, Lingjing, Liu, Lingyu, Bai, Zhongfei, Ye, Chen |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10150013/ https://www.ncbi.nlm.nih.gov/pubmed/37139522 http://dx.doi.org/10.3389/fnins.2023.1125230 |
Ejemplares similares
-
Subject-independent EEG classification based on a hybrid neural network
por: Zhang, Hao, et al.
Publicado: (2023) -
EEG-Based Brain Network Analysis of Chronic Stroke Patients After BCI Rehabilitation Training
por: Zhan, Gege, et al.
Publicado: (2022) -
Editorial: EMG/EEG Signals-Based Control of Assistive and Rehabilitation Robots
por: Gopura, R. A. R. C., et al.
Publicado: (2022) -
EEG Signal Complexity Measurements to Enhance BCI-Based Stroke Patients’ Rehabilitation
por: Al-Qazzaz, Noor Kamal, et al.
Publicado: (2023) -
Case report: post-stroke interventional BCI rehabilitation in an individual with preexisting sensorineural disability
por: Young, Brittany M., et al.
Publicado: (2014)