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Translational Neuroscience

Editor-in-Chief: David, Olivier

IMPACT FACTOR 2018: 2.038

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Brain-machine interfaces: an overview

Mikhail Lebedev
Published Online: 2014-03-28 | DOI: https://doi.org/10.2478/s13380-014-0212-z


Brain-machine interfaces (BMIs) hold promise to treat neurological disabilities by linking intact brain circuitry to assistive devices, such as limb prostheses, wheelchairs, artificial sensors, and computers. BMIs have experienced very rapid development in recent years, facilitated by advances in neural recordings, computer technologies and robots. BMIs are commonly classified into three types: sensory, motor and bidirectional, which subserve motor, sensory and sensorimotor functions, respectively. Additionally, cognitive BMIs have emerged in the domain of higher brain functions. BMIs are also classified as noninvasive or invasive according to the degree of their interference with the biological tissue. Although noninvasive BMIs are safe and easy to implement, their information bandwidth is limited. Invasive BMIs hold promise to improve the bandwidth by utilizing multichannel recordings from ensembles of brain neurons. BMIs have a broad range of clinical goals, as well as the goal to enhance normal brain functions.

Keywords: Brain; Decoding; Interface; Microstimulation; Monkey; Multielectrode; Neuroprosthetic; Paralysis; Plasticity; Robot

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About the article

Published Online: 2014-03-28

Published in Print: 2014-03-01

Citation Information: Translational Neuroscience, Volume 5, Issue 1, Pages 99–110, ISSN (Online) 2081-6936, DOI: https://doi.org/10.2478/s13380-014-0212-z.

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Frontiers in Systems Neuroscience, 2014, Volume 8
Mark A. Attiah and Martha J. Farah
Frontiers in Systems Neuroscience, 2014, Volume 8

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