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...

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Bibliographic Details
Published in:Neuron (Cambridge, Mass.) Mass.), 2014-11, Vol.84 (4), p.665-680
Main Authors: Shenoy, Krishna V., Carmena, Jose M.
Format: Article
Language:English
Subjects:
Algorithms
Brain - physiology
Brain research
Brain-Computer Interfaces
Humans
Motor Cortex - physiology
Movement - physiology
Neurons - physiology
Neurosciences
Prostheses
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Summary: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. [Display omitted] Shenoy and Carmena 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.
ISSN:0896-6273
1097-4199
DOI:10.1016/j.neuron.2014.08.038