Cargando…
Predicting Motor Imagery BCI Performance Based on EEG Microstate Analysis
Motor imagery (MI) electroencephalography (EEG) is natural and comfortable for controllers, and has become a research hotspot in the field of the brain–computer interface (BCI). Exploring the inter-subject MI-BCI performance variation is one of the fundamental problems in MI-BCI application. EEG mic...
Autores principales: | Cui, Yujie, Xie, Songyun, Fu, Yingxin, Xie, Xinzhou |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10526389/ https://www.ncbi.nlm.nih.gov/pubmed/37759889 http://dx.doi.org/10.3390/brainsci13091288 |
Ejemplares similares
-
An Online Data Visualization Feedback Protocol for Motor Imagery-Based BCI Training
por: Duan, Xu, et al.
Publicado: (2021) -
Task Transfer Learning for EEG Classification in Motor Imagery-Based BCI System
por: Zheng, Xuanci, et al.
Publicado: (2020) -
Classification of motor imagery EEG using deep learning increases performance in inefficient BCI users
por: Tibrewal, Navneet, et al.
Publicado: (2022) -
Using brain connectivity metrics from synchrostates to perform motor imagery classification in EEG-based BCI systems
por: Santamaria, Lorena, et al.
Publicado: (2018) -
Investigating User Proficiency of Motor Imagery for EEG-Based BCI System to Control Simulated Wheelchair
por: Saichoo, Theerat, et al.
Publicado: (2022)