Evidence map›Paper›PMID 38738213›Full record

ArticleAdvances in neural information processing systems2023

Plug-and-Play Stability for Intracortical Brain-Computer Interfaces: A One-Year Demonstration of Seamless Brain-to-Text Communication.

Chaofei Fan, Nick Hahn, Foram Kamdar, Donald Avansino, Guy H Wilson, Leigh Hochberg, Krishna V Shenoy, Jaimie M Henderson, Francis R Willett

Abstract read
In one paragraph

Article in Advances in neural information processing systems, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Article
  2. Article
  3. Explainability in AI-enabled medical neurotechnology: a scoping review.Journal of neuroengineering and rehabilitation · 2026
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. A Generalist Intracortical Motor Decoder.bioRxiv : the preprint server for biology · 2025
    Article
  9. Article
  10. Review
  11. Article
  12. Article
  13. An Accurate and Rapidly Calibrating Speech Neuroprosthesis.The New England journal of medicine · 2024
    Article
  14. The speech neuroprosthesis.Nature reviews. Neuroscience · 2024
    Review
  15. Article
  16. An accurate and rapidly calibrating speech neuroprosthesis.medRxiv : the preprint server for health sciences · 2024
    Article
  17. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Chaofei FanDepartment of Computer Science, Stanford University.
Nick HahnDepartment of Neurosurgery, Stanford University.
Foram KamdarDepartment of Neurosurgery, Stanford University.
Donald AvansinoHoward Hughes Medical Institute at Stanford University.
Guy H WilsonDepartment of Neuroscience, Stanford University.
Leigh HochbergSchool of Engineering and Carney Institute for Brain Science, Brown University.
Krishna V ShenoyBio-X Program, Stanford University.
Jaimie M HendersonDepartment of Neurosurgery, Stanford University.
Francis R WillettHoward Hughes Medical Institute at Stanford University.

Funding

Engaging new cognitive and motor signals to improve communication prosthesesR01DC014034 · NIDCD · STANFORD UNIVERSITY · PI JAIMIE M HENDERSON · 2015 to 2026
$8.2M
Intuitive, complete neural control of tablet computers for communicationU01DC017844 · NIDCD · BROWN UNIVERSITY · PI HOCHBERG, LEIGH R · 2019 to 2025
$4.3M
Dissecting distributed representations by advanced population activity analysis methods and modelingR01EB028171 · NIBIB · STANFORD UNIVERSITY · PI DRUCKMANN, SHAUL · 2019 to 2019
$1.1M
NIBIB NIH HHS R01 EB028171NIDCD NIH HHS R01 DC014034NIDCD NIH HHS U01 DC017844
6 · The paper itself

Abstract

Intracortical brain-computer interfaces (iBCIs) have shown promise for restoring rapid communication to people with neurological disorders such as amyotrophic lateral sclerosis (ALS). However, to maintain high performance over time, iBCIs typically need frequent recalibration to combat changes in the neural recordings that accrue over days. This requires iBCI users to stop using the iBCI and engage in supervised data collection, making the iBCI system hard to use. In this paper, we propose a method that enables self-recalibration of communication iBCIs without interrupting the user. Our method leverages large language models (LMs) to automatically correct errors in iBCI outputs. The self-recalibration process uses these corrected outputs ("pseudo-labels") to continually update the iBCI decoder online. Over a period of more than one year (403 days), we evaluated our Continual Online Recalibration with Pseudo-labels (CORP) framework with one clinical trial participant. CORP achieved a stable decoding accuracy of 93.84% in an online handwriting iBCI task, significantly outperforming other baseline methods. Notably, this is the longest-running iBCI stability demonstration involving a human participant. Our results provide the first evidence for long-term stabilization of a plug-and-play, high-performance communication iBCI, addressing a major barrier for the clinical translation of iBCIs.

Identifiers

PMID38738213
PMCPMC11086983

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Registered trials

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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.