Cited by 1,761EEG and Brain-Computer InterfacesMotor Control and AdaptationNeuroscience and Neural Engineering

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Learning to Control a Brain–Machine Interface for Reaching and Grasping by Primates

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Carmena, Jose M, Lebedev, Mikhail A, Crist, Roy E, O'Doherty, Joseph E, Santucci, David M, Dimitrov, Dragan F, Patil, Parag G, Henriquez, Craig S, Nicolelis, Miguel A. L·2003·PLoS Biology

DOI 10.1371/journal.pbio.0000042PMC PMC261882

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Abstract

Reaching and grasping in primates depend on the coordination of neural activity in large frontoparietal ensembles. Here we demonstrate that primates can learn to reach and grasp virtual objects by controlling a robot arm through a closed-loop brain-machine interface (BMIc) that uses multiple mathematical models to extract several motor parameters (i.e., hand position, velocity, gripping force, and the EMGs of multiple arm muscles) from the electrical activity of frontoparietal neuronal ensembles. As single neurons typically contribute to the encoding of several motor parameters, we observed that high BMIc accuracy required recording from large neuronal ensembles. Continuous BMIc operation by monkeys led to significant improvements in both model predictions and behavioral performance. Using visual feedback, monkeys succeeded in producing robot reach-and-grasp movements even when their arms did not move. Learning to operate the BMIc was paralleled by functional reorganization in multiple cortical areas, suggesting that the dynamic properties of the BMIc were incorporated into motor and sensory cortical representations.

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Authors: Carmena, J. M., Lebedev, M. A., Crist, R. E., O'Doherty, J. E., Santucci, D. M., Dimitrov, D. F., Patil, P. G., Henriquez, C. S., & Nicolelis, M. A. L.
Affiliations: Duke University (US)
Corresponding author: Nicolelis, Miguel A. L
ORCID: Carmena, Jose M
Lebedev, Mikhail A
Patil, Parag G
Henriquez, Craig S
Nicolelis, Miguel A. L
Venue: PLoS Biology
Publisher: Public Library of Science (PLoS)
Published: 2003-10-13
Volume: 1
Issue: 2
Pages: e42
ISSN: 1544-9173 (ISSN-L)
1545-7885
DOI: 10.1371/journal.pbio.0000042
PMC: PMC261882
OpenAlex: W2041248708
Cited by: 1,761
References: 43
Citations by year: 2026: 28 · 2025: 65 · 2024: 37 · 2023: 49 · 2022: 50 · 2021: 56 · 2020: 61 · 2019: 62 · 2018: 79 · 2017: 60 · 2016: 97 · 2015: 93 · 2014: 132 · 2013: 112 · 2012: 101
FWCI: 29.81
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (unspecified)
Locations: PLoS Biology (publishedVersion, cc-by)
PubMed Central (publishedVersion, other-oa)
PubMed (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: National Science Foundation
James S. McDonnell Foundation
Defense Advanced Research Projects Agency
Advanced Research Projects Agency
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
Keywords: brain-computer interface, reach-to-grasp, primate motor control, feedback control, motor learning, visual feedback
MeSH terms: Animals, Arm, Artificial Intelligence, Behavior, Animal, Biomechanical Phenomena, Biophysics, Brain, Brain Mapping, Electromyography, Electrophysiology, Female, Hand, Learning, Macaca, Models, Neurological, Models, Theoretical, Motor Activity, Motor Cortex, Movement, Neurons, Primates, Psychomotor Performance, Robotics, Somatosensory Cortex, Space Perception, Time Factors, Models, Statistical, Hand Strength, Biophysical Phenomena

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Learning to Control a Brain–Machine Interface for Reaching and Grasping by Primates

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