ArticleMedical engineering & physics2014
Wiener filtering of surface EMG with a priori SNR estimation toward myoelectric control for neurological injury patients.
Article in Medical engineering & physics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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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
6 citing papers in PubMed.
- AI-Driven Hemodynamic Detection of Self-Induced Daydreaming With EMG-Based Physiological Triggers During Pre- and Post-Prandial States Using fNIRS and EGG.CNS neuroscience & therapeutics · 2026Article
- Botulinum toxin treatment may improve myoelectric pattern recognition in robot-assisted stroke rehabilitation.Frontiers in neuroscience · 2024Article
- Reducing Noise, Artifacts and Interference in Single-Channel EMG Signals: A Review.Sensors (Basel, Switzerland) · 2023Review
- Properties of the surface electromyogram following traumatic spinal cord injury: a scoping review.Journal of neuroengineering and rehabilitation · 2021Article
- Nondestructive Estimation of Muscle Contributions to STS Training with Different Loadings Based on Wearable Sensor System.Sensors (Basel, Switzerland) · 2018Article
- EMG-Based Continuous and Simultaneous Estimation of Arm Kinematics in Able-Bodied Individuals and Stroke Survivors.Frontiers in neuroscience · 2017Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Voluntary surface electromyogram (EMG) signals from neurological injury patients are often corrupted by involuntary background interference or spikes, imposing difficulties for myoelectric control. We present a novel framework to suppress involuntary background spikes during voluntary surface EMG recordings. The framework applies a Wiener filter to restore voluntary surface EMG signals based on tracking a priori signal to noise ratio (SNR) by using the decision-directed method. Semi-synthetic surface EMG signals contaminated by different levels of involuntary background spikes were constructed from a database of surface EMG recordings in a group of spinal cord injury subjects. After the processing, the onset detection of voluntary muscle activity was significantly improved against involuntary background spikes. The magnitude of voluntary surface EMG signals can also be reliably estimated for myoelectric control purpose. Compared with the previous sample entropy analysis for suppressing involuntary background spikes, the proposed framework is characterized by quick and simple implementation, making it more suitable for application in a myoelectric control system toward neurological injury rehabilitation.
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Registered trials
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