Evidence map›Paper›PMID 41857304›Full record

ReviewVisual computing for industry, biomedicine, and art2026

Review of electroencephalography and electromyography research in robotics: opportunities and challenges.

Zefeng Wang, Meiyan Xu, Junfeng Yao, Yue Yu, Bingbing Hu, Yufei Wang, Yu Wang, Xiaopeng Zhang

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Visual computing for industry, biomedicine, and art, 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

8 authors.

Zefeng Wang *School of Informatics, Xiamen University, Xiamen, 361102, Fujian, China.
Meiyan Xu *School of Computer Science, Minnan Normal University, Zhangzhou, 363000, Fujian, China.
Junfeng YaoSchool of Informatics, Xiamen University, Xiamen, 361102, Fujian, China. yao0010@xmu.edu.cn.ORCID http://orcid.org/0000-0002-2330-7406
Yue YuSchool of Computer Science, Minnan Normal University, Zhangzhou, 363000, Fujian, China.
Bingbing HuSchool of Film, Xiamen University, Xiamen, 361005, Fujian, China.
Yufei WangSchool of Film, Xiamen University, Xiamen, 361005, Fujian, China.
Yu WangSchool of Medicine, Dongfang Hospital of Xiamen University, Xiamen University, Xiamen, 361102, Fujian, China.
Xiaopeng ZhangInstitute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.

Funding

Fujian Provincial Science and Technology Major Project 2024HZ01040037Jiangxi Provincial Natural Science Foundation Key Project 20244BAB28039National Natural Science Foundation of China 62072388Xiamen Public Technology Service Platform 3502Z20231043
6 · The paper itself

Abstract

In the evolving nexus of neuroscience and robotics, the symbiotic fusion of electroencephalography (EEG) and electromyography (EMG) is emerging as a paradigm-shifting avenue for enhancing human-machine interfaces. While EEG, which captures the subtle electrical nuances of the brain, offers a potent channel for nuanced brain-machine communication, EMG serves as a bridge, converting neuromuscular intentions into actionable directives for robotic apparatuses. This review highlights the current methodologies in which EEG and EMG not only function in silos but also converge harmoniously to dictate robotic control. By delving deeper into this, the intricate synergy between cognitive processes, muscular responses, and machine actions can be unraveled. Subsequently, the discourse also navigates through the myriad challenges encountered in realizing real-time, seamless integration of these bio-signals with robotics and the innovative solutions poised to address them. The aim is to provide a comprehensive understanding of the interplay between neuroscience and robotics. This insight will help drive breakthroughs in adaptive human-machine collaboration.

Indexed as

Data acquisitionElectroencephalographyElectromyographyHuman-machine interfaceNeuromuscular intentionsNeuroscienceRobotics

Identifiers

PMID41857304
PMCPMC13003060

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.