Cargando…
Hybrid Human-Machine Interface for Gait Decoding Through Bayesian Fusion of EEG and EMG Classifiers
Despite the advances in the field of brain computer interfaces (BCI), the use of the sole electroencephalography (EEG) signal to control walking rehabilitation devices is currently not viable in clinical settings, due to its unreliability. Hybrid interfaces (hHMIs) represent a very recent solution t...
Autores principales: | Tortora, Stefano, Tonin, Luca, Chisari, Carmelo, Micera, Silvestro, Menegatti, Emanuele, Artoni, Fiorenzo |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7705173/ https://www.ncbi.nlm.nih.gov/pubmed/33281593 http://dx.doi.org/10.3389/fnbot.2020.582728 |
Ejemplares similares
-
Editorial: Hybrid brain-robot interfaces for enhancing mobility
por: Tortora, Stefano, et al.
Publicado: (2023) -
Effective Synchronization of EEG and EMG for Mobile Brain/Body Imaging in Clinical Settings
por: Artoni, Fiorenzo, et al.
Publicado: (2018) -
Unidirectional brain to muscle connectivity reveals motor cortex control of leg muscles during stereotyped walking
por: Artoni, Fiorenzo, et al.
Publicado: (2017) -
Delta Power Is Higher and More Symmetrical in Ischemic Stroke Patients with Cortical Involvement
por: Fanciullacci, Chiara, et al.
Publicado: (2017) -
Connectivity Measures Differentiate Cortical and Subcortical Sub-Acute Ischemic Stroke Patients
por: Fanciullacci, Chiara, et al.
Publicado: (2021)