Evidence map›Paper›PMID 38238386›Full record

ArticleScientific reports2024

Brain control of bimanual movement enabled by recurrent neural networks.

Darrel R Deo, Francis R Willett, Donald T Avansino, Leigh R Hochberg, Jaimie M Henderson, Krishna V Shenoy

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
–field-weighted citation impact
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

22 citing papers in PubMed.

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  16. A Generalist Intracortical Motor Decoder.bioRxiv : the preprint server for biology · 2025
    Article
  17. Article
  18. Article
  19. Speech motor cortex enables BCI cursor control and click.bioRxiv : the preprint server for biology · 2024
    Article
  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Darrel R DeoDepartment of Neurosurgery, Stanford University, Stanford, CA, USA. ddeo@stanford.edu.
Francis R WillettHoward Hughes Medical Institute at Stanford University, Stanford, CA, USA.
Donald T AvansinoHoward Hughes Medical Institute at Stanford University, Stanford, CA, USA.
Leigh R HochbergSchool of Engineering, Brown University, Providence, RI, USA.
Jaimie M Henderson *Department of Neurosurgery, Stanford University, Stanford, CA, USA.
Krishna V Shenoy *Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA.

Funding

Engaging new cognitive and motor signals to improve communication prosthesesR01DC014034 · NIDCD · STANFORD UNIVERSITY · PI JAIMIE M HENDERSON · 2015 to 2026
$8.2M
Cortical basis of complex motor sequences in humans for neural interfacesU01NS123101 · NINDS · STANFORD UNIVERSITY · PI HENDERSON, JAIMIE M · 2021 to 2025
$6.3M
NIDCD NIH HHS R01 DC014034NIDCD NIH HHS R01-DC014034NINDS NIH HHS U01 NS123101NINDS NIH HHS U01-NS123101
6 · The paper itself

Abstract

Brain-computer interfaces have so far focused largely on enabling the control of a single effector, for example a single computer cursor or robotic arm. Restoring multi-effector motion could unlock greater functionality for people with paralysis (e.g., bimanual movement). However, it may prove challenging to decode the simultaneous motion of multiple effectors, as we recently found that a compositional neural code links movements across all limbs and that neural tuning changes nonlinearly during dual-effector motion. Here, we demonstrate the feasibility of high-quality bimanual control of two cursors via neural network (NN) decoders. Through simulations, we show that NNs leverage a neural 'laterality' dimension to distinguish between left and right-hand movements as neural tuning to both hands become increasingly correlated. In training recurrent neural networks (RNNs) for two-cursor control, we developed a method that alters the temporal structure of the training data by dilating/compressing it in time and re-ordering it, which we show helps RNNs successfully generalize to the online setting. With this method, we demonstrate that a person with paralysis can control two computer cursors simultaneously. Our results suggest that neural network decoders may be advantageous for multi-effector decoding, provided they are designed to transfer to the online setting.

Indexed as

Brain-Computer InterfacesNeural Networks, ComputerBrainFunctional LateralityHandHumansMovementParalysis

Identifiers

PMID38238386
PMCPMC10796685

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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.