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Learning to Control a Brain–Machine Interface for Reaching and Grasping by Primates
Open accessAbstract
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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“In our context, the perhaps most interesting observation in this experiment (see Carmena et al., 2003, for details) is how the monkey gradually begins to neglect his original arm, which is, after all, a part of his biological body.”
From Empirical perspectives from the self-model theory of subjectivity: a brief summary with examples (2007) · cites this paper as Carmena et al., 2003
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“Another similar, overstated extrapolation regards evidence from invasive and semi-invasive BCI, where learning and co-adaptation have been well documented [19,23,24,57–59].”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [58]
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Metadata
- 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
- 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
- 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
- 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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