ArticleFrontiers in neuroscience2022
Design and Evaluation of a Custom-Made Electromyographic Biofeedback System for Facial Rehabilitation.
Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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.
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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.
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Who cites it
2 citing papers in PubMed, 5 citations in OpenAlex.
- Automated Neuromuscular Assessment: Machine-Learning-Based Facial Palsy Classification Using Surface Electromyography.Sensors (Basel, Switzerland) · 2025Article
- Surface Electromyographic Features for Severity Classification in Facial Palsy: Insights from a German Cohort and Implications for Future Biofeedback Use.Sensors (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: In the rehabilitation of postoperative facial palsy, physical therapy is of paramount importance. However, in the early rehabilitation phase, voluntary movements are often limited, and thus, the motivation of patients is impacted. In these situations, biofeedback of facial electromyographic (EMG) signals enables the visual representation of the rehabilitation progress, even without apparent facial movements. In the present study, we designed and evaluated a custom-made EMG biofeedback system enabling cost-effective facial rehabilitation. Methods: This prospective study describes a custom-made EMG system, consisting of a microcontroller board and muscle sensors, which was used to record the EMG of Results: In healthy subjects, a good correlation was measured between the mean EMG amplitudes of the custom-made and commercial EMG device for both Conclusion: The present study demonstrates a good application potential of our custom-made EMG biofeedback device to detect facial EMG activity in healthy subjects as well as patients with facial palsies. There is a correlation between the electrophysiological measurements and the clinical outcome. Such a device might enable cost-efficient home-based facial EMG biofeedback. However, movement detection accuracy should be improved in future studies to reach ranges of commercial devices.
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