Combining decoder design and neural adaptation in brain-machine interfaces
Brain-machine interfaces (BMIs) aim to help people with paralysis by decoding movement-related neural signals into control signals for guiding computer cursors, prosthetic arms, and other assistive devices. Despite compelling laboratory experiments and ongoing FDA pilot clinical trials, system perfo...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2014 |
| País: | España |
| Institución: | Universidad Católica de Valencia San Vicente Mártir |
| Repositorio: | RIUCV. Repositorio de la Universidad Católica de Valencia San Vicente Mártir |
| Idioma: | inglés |
| OAI Identifier: | oai:riucv.ucv.es:20.500.12466/7035 |
| Acceso en línea: | https://hdl.handle.net/20.500.12466/7035 |
| Access Level: | acceso abierto |
| Palabra clave: | Brain-machine interfaces Paralysis Decoder design Neural adaptation 32 Ciencias Médicas |
| Sumario: | Brain-machine interfaces (BMIs) aim to help people with paralysis by decoding movement-related neural signals into control signals for guiding computer cursors, prosthetic arms, and other assistive devices. Despite compelling laboratory experiments and ongoing FDA pilot clinical trials, system performance, robustness, and generalization remain challenges. We provide a perspective on how two complementary lines of investigation, that have focused on decoder design and neural adaptation largely separately, could be brought together to advance BMIs. This BMI paradigm should also yield new scientific insights into the function and dysfunction of the nervous system. |
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