Evidence map›Paper›PMID 14624244›Full record

ArticlePLoS biology2003

Learning to control a brain-machine interface for reaching and grasping by primates.

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

Open access · goldAbstract read
In one paragraph

Article in PLoS biology, 2003. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 579 papers.

0numbers the graph read from it
0cells of the map it votes in
579citing papers in PubMed
27.2field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

579 citing papers in PubMed, 1,775 citations in OpenAlex.

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  20. Neural trajectories improve motor precision.bioRxiv : the preprint server for biology · 2025
    Article

519 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors at 1 institution in 1 country.

Jose M CarmenaDepartment of Neurobiology, Duke University, Durham, North Carolina, USA.
Mikhail A Lebedev
Roy E Crist
Joseph E O'Doherty
David M Santucci
Dragan F Dimitrov
Parag G Patil
Craig S Henriquez
Miguel A L Nicolelis
Duke University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Biomechanical PhenomenaBiophysicsHand StrengthAnimalsArmArtificial IntelligenceBehavior, AnimalBiophysical PhenomenaBrainBrain MappingElectromyographyElectrophysiologyFemaleHandLearningMacaca

Identifiers

PMID14624244
PMCPMC261882
OpenAlexW2041248708

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.