Cargando…
Teaching brain-machine interfaces as an alternative paradigm to neuroprosthetics control
Brain-machine interfaces (BMI) usually decode movement parameters from cortical activity to control neuroprostheses. This requires subjects to learn to modulate their brain activity to convey all necessary information, thus imposing natural limits on the complexity of tasks that can be performed. He...
Autores principales: | Iturrate, Iñaki, Chavarriaga, Ricardo, Montesano, Luis, Minguez, Javier, Millán, José del R. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2015
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4564803/ https://www.ncbi.nlm.nih.gov/pubmed/26354145 http://dx.doi.org/10.1038/srep13893 |
Ejemplares similares
-
Errare machinale est: the use of error-related potentials in brain-machine interfaces
por: Chavarriaga, Ricardo, et al.
Publicado: (2014) -
Exploiting Task Constraints for Self-Calibrated Brain-Machine Interface Control Using Error-Related Potentials
por: Iturrate, Iñaki, et al.
Publicado: (2015) -
A data-driven machine learning approach for brain-computer interfaces targeting lower limb neuroprosthetics
por: Dillen, Arnau, et al.
Publicado: (2022) -
Blending of brain-machine interface and vision-guided autonomous robotics improves neuroprosthetic arm performance during grasping
por: Downey, John E., et al.
Publicado: (2016) -
Control of an Ambulatory Exoskeleton with a Brain–Machine Interface for Spinal Cord Injury Gait Rehabilitation
por: López-Larraz, Eduardo, et al.
Publicado: (2016)