Leveraging neural dynamics to extend functional lifetime of brain-machine interfaces
Abstract Intracortical brain-machine interfaces (BMIs) aim to restore lost motor function to people with neurological deficits by decoding neural activity into control signals for guiding prostheses. An important challenge facing BMIs is that, over time, the number of neural signals recorded from im...
Guardado en:
Autores principales: | Jonathan C. Kao, Stephen I. Ryu, Krishna V. Shenoy |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2017
|
Materias: | |
Acceso en línea: | https://doaj.org/article/a9d7b426316a4e178dc3790053796953 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Making brain–machine interfaces robust to future neural variability
por: David Sussillo, et al.
Publicado: (2016) -
Correction: Corrigendum: Making brain-machine interfaces robust to future neural variability
por: David Sussillo, et al.
Publicado: (2017) -
Measurement, manipulation and modeling of brain-wide neural population dynamics
por: Krishna V. Shenoy, et al.
Publicado: (2021) -
Neural signal analysis with memristor arrays towards high-efficiency brain–machine interfaces
por: Zhengwu Liu, et al.
Publicado: (2020) -
Leveraging neural properties to inform stimulation
por: Hayriye Cagnan
Publicado: (2021)