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Neuron. 2014 Nov 19;84(4):665-80. doi: 10.1016/j.neuron.2014.08.038. Epub 2014 Nov 19.

Combining decoder design and neural adaptation in brain-machine interfaces.

Author information

1
Departments of Electrical Engineering, Bioengineering & Neurobiology, Stanford Neurosciences Institute and Bio-X Program, Stanford University, Stanford, California 94305, USA. Electronic address: shenoy@stanford.edu.
2
Department of Electrical Engineering and Computer Sciences and Helen Wills Neuroscience Institute, University of California at Berkeley, Berkeley, California 94704, USA. Electronic address: jcarmena@berkeley.edu.

Abstract

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.

PMID:
25459407
DOI:
10.1016/j.neuron.2014.08.038
[Indexed for MEDLINE]
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