Evidence map›Paper›PMID 41221369›Full record

ArticleNature machine intelligence2025

Brain-computer interface control with artificial intelligence copilots.

Johannes Y Lee, Sangjoon Lee, Abhishek Mishra, Xu Yan, Brandon McMahan, Brent Gaisford, Charles Kobashigawa, Mike Qu, Chang Xie, Jonathan C Kao

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature machine intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
–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

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Full-Stack Architectures for Intelligent Brain-Computer Interfaces.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
  6. Review
  7. Article
  8. Review
  9. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Johannes Y LeeDepartment of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA.ORCID 0000-0003-2420-4916
Sangjoon LeeDepartment of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA.
Abhishek MishraDepartment of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA.ORCID 0000-0002-1933-1251
Xu YanDepartment of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA.
Brandon McMahanDepartment of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA.
Brent GaisfordDepartment of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA.
Charles KobashigawaDepartment of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA.
Mike QuDepartment of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA.
Chang XieDepartment of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA.
Jonathan C KaoDepartment of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA.ORCID 0000-0002-9298-0143

Funding

Next generation brain-machine interfaces controlled synergistically with artificial intelligenceDP2NS122037 · NINDS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI KAO, JONATHAN CHAU-YAN · 2020 to 2020
$2.3M
An Open Source Simulator for Multi Degree-Of-Freedom Brain-Machine InterfacesR01NS121097 · NINDS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI KAO, JONATHAN CHAU-YAN · 2021 to 2025
$1.9M
NINDS NIH HHS DP2 NS122037NINDS NIH HHS R01 NS121097
6 · The paper itself

Abstract

Motor brain-computer interfaces (BCIs) decode neural signals to help people with paralysis move and communicate. Even with important advances in the past two decades, BCIs face a key obstacle to clinical viability: BCI performance should strongly outweigh costs and risks. To significantly increase the BCI performance, we use shared autonomy, where artificial intelligence (AI) copilots collaborate with BCI users to achieve task goals. We demonstrate this AI-BCI in a non-invasive BCI system decoding electroencephalography signals. We first contribute a hybrid adaptive decoding approach using a convolutional neural network and ReFIT-like Kalman filter, enabling healthy users and a participant with paralysis to control computer cursors and robotic arms via decoded electroencephalography signals. We then design two AI copilots to aid BCI users in a cursor control task and a robotic arm pick-and-place task. We demonstrate AI-BCIs that enable a participant with paralysis to achieve 3.9-times-higher performance in target hit rate during cursor control and control a robotic arm to sequentially move random blocks to random locations, a task they could not do without an AI copilot. As AI copilots improve, BCIs designed with shared autonomy may achieve higher performance.

Identifiers

PMID41221369
PMCPMC12599856

What OpenQuestion holds

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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.