Evidence map›Paper›PMID 42093854›Full record

ArticleIBRO neuroscience reports2026

Mapping knowledge structure and emerging trends in non-invasive brain-computer interface for stroke rehabilitation.

Ying Li, Jiaying Chen, Yu Wang, Jinghui Huang, Fanfu Fang

Abstract read
In one paragraph

Article in IBRO neuroscience reports, 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

5 authors.

Ying LiDepartment of Rehabilitation Medicine, The First Affiliated Hospital of Naval Medical University, Shanghai, China.
Jiaying ChenDepartment of Rehabilitation Medicine, The First Affiliated Hospital of Naval Medical University, Shanghai, China.
Yu WangDepartment of Rehabilitation Medicine, The First Affiliated Hospital of Naval Medical University, Shanghai, China.
Jinghui HuangDepartment of Rehabilitation Medicine, The First Affiliated Hospital of Naval Medical University, Shanghai, China.
Fanfu FangDepartment of Rehabilitation Medicine, The First Affiliated Hospital of Naval Medical University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To explore the current research landscape and emerging frontiers in the application of non‑invasive brain-computer interface (BCI) technology in the field of stroke. Methods: Publications related to non‑invasive BCI technology in stroke were retrieved from the Web of Science Core Collection database between January 2014 and March 2025. Only English articles and reviews were included; conference papers, editorials, and corrections were excluded.Bibliometric software was employed to construct visual knowledge maps based on authors, institutions, keywords, and other metrics. Results: After excluding items such as publisher corrections, editorial materials, and conference papers, 587 publications were included. Over the past decade, the annual number of publications showed an upward trend. China (177 publications) contributed the highest volume of output globally. The most prolific author was Jochumsen, Mads (17 publications), and Aalborg University (31 publications) was the leading institution. The journal with the highest number of publications was IEEE Transactions on Neural Systems and Rehabilitation Engineering(60 articles), while the Journal of Neural Engineeringreceived the most citations (2129). Keyword analysis and burst detection revealed that research hotspots mainly focus on signal acquisition methods, EEG‑based signal types, neural mechanisms, algorithms, external devices, and their impact on functional rehabilitation after stroke. Conclusion: Over the past ten years, advances in technology and interdisciplinary collaboration between medicine and engineering have provided new opportunities for stroke rehabilitation through non‑invasive BCI. This technology shows great clinical value in promoting neural plasticity and functional recovery in stroke patients.It is projected that future research will emphasize multimodal integration, innovations in algorithms such as deep learning, and breakthroughs in material technology, which are expected to represent major research directions and hotspots in the field.

Indexed as

BibliometricsBrain–computer interfaceNeurotechnologyRehabilitationStroke

Identifiers

PMID42093854
PMCPMC13141469

What OpenQuestion holds

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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.