Evidence map›Paper›PMID 41855458›Full record

ReviewAnnals of medicine2026

Application and prospects of brain-computer interface technology for motor function reconstruction after brachial plexus injury.

Su Song, Xia Li, Pinglei Pan

Abstract readReview
In one paragraph

Review in Annals of medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Su SongDepartment of Earth Sciences, University College London.
Xia LiResearch Management Department, Affiliated Hospital 6 of Nantong University, Yancheng Third People's Hospital, Yancheng, PR China.ORCID 0000-0002-2471-3561
Pinglei PanResearch Management Department, Affiliated Hospital 6 of Nantong University, Yancheng Third People's Hospital, Yancheng, PR China.ORCID 0000-0002-2659-7664

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBrachial plexus injury (BPI) is a severe peripheral nerve disorder leading to significant upper limb motor dysfunction. While traditional surgeries like nerve grafting and tendon transfer exist, functional outcomes are often suboptimal due to biomechanical limitations and slow neural recovery. Brain-computer interface (BCI) technology has emerged as a promising innovative pathway for motor function reconstruction.

objectiveThis review systematically evaluates the current applications, physiological mechanisms, and technical challenges of BCI technology specifically within the clinical framework of BPI rehabilitation.

methodsWe analysed recent research breakthroughs focusing on neural repair mechanisms, clinical translational applications of BCI-controlled neuroprosthetics, and the integration of novel biomaterials.

resultsBCI technology facilitates cortical remapping after standard BPI procedures like nerve transfers by providing synchronised closed-loop feedback. Unlike applications for amputees that drive external prosthetics, BCI in BPI focuses on in-situ muscle activation

conclusionBCI technology represents a transformative approach for BPI rehabilitation, moving from mechanical substitution to biological reactivation. Overcoming technical barriers in signal reliability and establishing personalised rehabilitation systems are essential for their broad clinical translation.

Indexed as

Brachial PlexusBrachial Plexus NeuropathiesBrain-Computer InterfacesHumansNerve TransferRecovery of Functionbrachial plexus nerve injuryBrain-computer interfacesmotor function reconstructionneural rehabilitationneuroprosthetics

Identifiers

PMID41855458
PMCPMC13003872

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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.