Evidence map›Paper›PMID 38686330›Full record

ReviewFrontiers in neuroscience2024

Flexible high-density microelectrode arrays for closed-loop brain-machine interfaces: a review.

Xiang Liu, Yan Gong, Zebin Jiang, Trevor Stevens, Wen Li

Abstract readReview
In one paragraph

Review in Frontiers in neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Article
  2. Full-Stack Architectures for Intelligent Brain-Computer Interfaces.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
  3. Article
  4. Review
  5. Article
  6. Article
  7. Review
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  11. Review
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  13. Article
  14. UnravelingFrontiers in bioengineering and biotechnology · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Xiang LiuNeuroscience Program, Department of Physiology, Michigan State University, East Lansing, MI, United States.
Yan GongInstitute for Quantitative Health Science and Engineering (IQ), East Lansing, MI, United States.
Zebin JiangInstitute for Quantitative Health Science and Engineering (IQ), East Lansing, MI, United States.
Trevor StevensDepartment of Electrical and Computer Engineering, Michigan State University, East Lansing, MI, United States.
Wen LiNeuroscience Program, Department of Physiology, Michigan State University, East Lansing, MI, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Flexible high-density microelectrode arrays (HDMEAs) are emerging as a key component in closed-loop brain-machine interfaces (BMIs), providing high-resolution functionality for recording, stimulation, or both. The flexibility of these arrays provides advantages over rigid ones, such as reduced mismatch between interface and tissue, resilience to micromotion, and sustained long-term performance. This review summarizes the recent developments and applications of flexible HDMEAs in closed-loop BMI systems. It delves into the various challenges encountered in the development of ideal flexible HDMEAs for closed-loop BMI systems and highlights the latest methodologies and breakthroughs to address these challenges. These insights could be instrumental in guiding the creation of future generations of flexible HDMEAs, specifically tailored for use in closed-loop BMIs. The review thoroughly explores both the current state and prospects of these advanced arrays, emphasizing their potential in enhancing BMI technology.

Indexed as

brain-machine interfacesclosed-loopflexibilityhigh densitymicroelectrode array

Identifiers

PMID38686330
PMCPMC11057246

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.