1 Introduction
Stroke ranks among the most prevalent diseases globally, posing a significant threat to life and health. Despite variations in healthcare systems across nations, addressing the burden of stroke remains a global priority (1). Bibliometric analysis plays an increasingly vital role in medical research, primarily due to its ability to quantitatively and qualitatively assess research trends and hotspots within specific disciplines (2). This method efficiently analyzes the quantitative characteristics and patterns of literature in stroke research, providing directional guidance and reference for future studies. However, greater emphasis should be placed on methodological rigor and scientific validity when conducting bibliometric research.
2 Commentary
We read with great interest the publication edited by Liao et al. (3) titled “Bibliometric analysis of surface electromyography trends in stroke rehabilitation research,” which was published in the issue of Frontiers in Neurology. By using bibliometrics, this study has identified the countries, top institutions, journals, and researchers globally involved in sEMG research on stroke. Keyword analysis shows that “rehabilitation” and “recovery” are key research themes, reflecting a focus on improving post-stroke functional outcomes. The analysis reveals that future research should focus on the clinical utility of sEMG, including cost-effectiveness and integration into clinical workflows, while developing clear application guidelines. We highly support and appreciate the researchers' studies, and we thank them for their contributions to the field. However, we identified several points requiring clarification and revision.
First, as Figure 1 showed, the study used non-standard inclusion criteria in its bibliometric analysis, incorporating diverse document types such as editorials, letters, conference abstracts, and conference papers while excluding reviews and early access reviews. Reviews are crucial for mapping a field's academic landscape and emerging trends, providing a more accurate reflection of significant directions and research hotspots within a specific domain. Editorials and conference abstracts typically represent preliminary or non-original research. This selection may skew the dataset, potentially inflating publication counts for certain institutions or authors without corresponding contributions to substantive research environments. We strongly question this methodological decision, and the original authors should provide explicit justification. We recommend the authors rerun key analyses (e.g., top institutions, prolific authors, and keywords) using a more traditional dataset (excluding document types other than “articles” and “reviews”) to demonstrate the robustness—or lack thereof—of their primary conclusions. Bibliometric studies, such as Zhang et al., provide valuable comparative benchmarks for sound methodology and deeper thematic analysis (4).
Figure 1
The flowchart of papers identification and selection.
Second, the original study presents contradictory narratives regarding author influence. In the text, Lay B.S. is rated as the “most influential” author based on 120 total citations. However, Figure 2 in the same section visually represents author influence using the H-index, where Zhang X and Zhou P lead with an H-index of 11. The absence of a unifying framework in these statements, which are based on disparate metrics, results in a state of perplexity. This represents a substantial deficiency in the processes of data interpretation and reporting. Additionally, the study's evaluation of author influence is insufficient and incomplete, relying solely on a limited set of metrics (publication volume and H-index). We advocate that robust bibliometric assessments of author impact require transparent, multi-metric approaches (e.g., total citations, average citation count, citations per paper, H-index, and G-index) rather than selective reporting. The original text's assertion about “most influential authors” currently lacks substantiation and necessitates correction or a more nuanced explanation.
Figure 2
Authors' Local Impact by H-index. H-index is an index used to measure the academic influence of scholars, scientists or researchers in bibliometrics. Specifically, if a scholar's H-index is 10, it means that the scholar has at least 10 papers, each of which has been cited at least 10 times by other scholars.
Third, the original study's keyword analysis was limited to listing high-frequency terms such as “stroke” and “rehabilitation.” This approach fails to reveal the field's dynamic knowledge structure beyond a superficial snapshot. Specifically, we recommend that the original authors conduct keyword co-occurrence network analysis and clustering to map distinct research sub-themes (e.g., “robot-assisted therapy,” “brain-computer interfaces,” and “muscle synergies”). Furthermore, analyzing thematic evolution across different time periods is crucial for visualizing shifts in research focus (e.g., from basic assessment to technology integration). Such deeper analysis transforms simple counts into meaningful insights about past trends and future directions.
The research indicates that future studies should focus on clinical utility, however its discussion fails to use bibliometric findings to critically analyze specific barriers to clinical application. It is recommended that the authors synthesize their results, including the prominence of engineering-focused, highly cited papers vs. patient-centered outcome research, to structure a discussion on translational challenges. These may include the lack of standardized sEMG protocols, cost-effectiveness studies, clinical practice guidelines, and interoperability with routine clinical workflows.
In summary, the authors' contributions to the study of surface electromyography trends in stroke rehabilitation are acknowledged. It is further recommended that more rigorous research methodologies and evaluation metrics be adopted to enhance the study's scientific rigor.
Statements
Author contributions
HZ: Conceptualization, Investigation, Methodology, Visualization, Writing – original draft, Writing – review & editing. HL: Investigation, Methodology, Software, Writing – original draft, Writing – review & editing. MH: Conceptualization, Investigation, Methodology, Supervision, Writing – original draft, Writing – review & editing. ZZ: Conceptualization, Investigation, Supervision, Visualization, Writing – original draft, Writing – review & editing.
Funding
The author(s) declared that financial support was not received for this work and/or its publication.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
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References
1.
Ji C Ge X Zhang J Tong H . The stroke burden in China and its long-term trends: insights from the global burden of disease (GBD) study 1990-2021. Nutr Metab Cardiovasc Dis. (2025) 35:103848. doi: 10.1016/j.numecd.2025.103848
2.
Wang W Chi X Ji J Zhang J Xiang S Xiong B et al . Global status and trends of invasive pulmonary aspergillosis: a bibliometric study. Medicine (Baltimore). (2025) 104:e42603. doi: 10.1097/MD.0000000000042603
3.
Liao ZP Li JH Wu FC Xia YX Li YZ Ma LN et al . Bibliometric analysis of surface electromyography trends in stroke rehabilitation research. Front Neurol. (2025) 16:1568797. doi: 10.3389/fneur.2025.1568797
4.
Zhang J Ji C Zhai X Ren S Tong H . Global trends and hotspots in research on acupuncture for stroke: a bibliometric and visualization analysis. Eur J Med Res. (2023) 28:359. doi: 10.1186/s40001-023-01253-w
Summary
Keywords
bibliometric analysis, clinical application, rehabilitation, sEMG, stroke
Citation
Liu H, Huang M, Zhang Z and Zhao H (2026) Commentary: Bibliometric analysis of surface electromyography trends in stroke rehabilitation research. Front. Neurol. 17:1771446. doi: 10.3389/fneur.2026.1771446
Received
19 December 2025
Revised
16 January 2026
Accepted
16 January 2026
Published
02 February 2026
Volume
17 - 2026
Edited by
Himel Mondal, All India Institute of Medical Sciences, Deoghar (AIIMS Deoghar), India
Reviewed by
Jiale Zhang, China Science and Technology Development Center for Chinese Medicine, China
Updates
Copyright
© 2026 Liu, Huang, Zhang and Zhao.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Zaixiang Zhang, ga20250730@163.com; Hua Zhao, 1501282990@qq.com
†These authors have contributed equally to this work and share first authorship
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.