ArticleJournal of neurophysiology2014
Error reduction in EMG signal decomposition.
Article in Journal of neurophysiology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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.
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.
Who cites it
16 citing papers in PubMed.
- Spiking neural network decoders of finger forces from high-density intramuscular microelectrode arrays.Nature communications · 2026Article
- Blood flow restriction modulates common drive to motor units and force precision: implications for neuromuscular coordination.European journal of applied physiology · 2026Article
- Sample Preparation and Phosphopeptide Enrichment for Plant Phosphoproteomics via Label-Free Mass Spectrometry.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Differential impact on motor unit characteristics across severities of adult spinal muscular atrophy.Annals of clinical and translational neurology · 2023Article
- The Effect of Local Cooling at the Elbow on Nerve Conduction Velocity and Motor Unit Behaviour: An Exploration of a Novel Neurological Assessment.Sensors (Basel, Switzerland) · 2021Article
- Characteristics of the Electrophysiological Properties of Neuromuscular Motor Units and Its Adaptive Strategy Response in Lower Extremity Muscles for Seniors with Pre-Sarcopenia: A Preliminary Study.International journal of environmental research and public health · 2021Article
- Fatigue-related modulation of low-frequency common drive to motor units.European journal of applied physiology · 2020Article
- High-threshold motor unit firing reflects force recovery following a bout of damaging eccentric exercise.PloS one · 2018Article
- Increased Force Variability Is Associated with Altered Modulation of the Motorneuron Pool Activity in Autism Spectrum Disorder (ASD).International journal of molecular sciences · 2017Article
- Motor Unit Activity during Fatiguing Isometric Muscle Contraction in Hemispheric Stroke Survivors.Frontiers in human neuroscience · 2017Article
- Article
- Synchronization of motor unit firings: an epiphenomenon of firing rate characteristics not common inputs.Journal of neurophysiology · 2016Article
- Clarification of methods used to validate surface EMG decomposition algorithms as described by Farina et al. (2014).Journal of applied physiology (Bethesda, Md. : 1985) · 2015Article
- Decomposition of surface EMG signals from cyclic dynamic contractions.Journal of neurophysiology · 2015Article
- Phosphoproteomics technologies and applications in plant biology research.Frontiers in plant science · 2015Review
- Statistically rigorous calculations do not support common input and long-term synchronization of motor-unit firings.Journal of neurophysiology · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Decomposition of the electromyographic (EMG) signal into constituent action potentials and the identification of individual firing instances of each motor unit in the presence of ambient noise are inherently probabilistic processes, whether performed manually or with automated algorithms. Consequently, they are subject to errors. We set out to classify and reduce these errors by analyzing 1,061 motor-unit action-potential trains (MUAPTs), obtained by decomposing surface EMG (sEMG) signals recorded during human voluntary contractions. Decomposition errors were classified into two general categories: location errors representing variability in the temporal localization of each motor-unit firing instance and identification errors consisting of falsely detected or missed firing instances. To mitigate these errors, we developed an error-reduction algorithm that combines multiple decomposition estimates to determine a more probable estimate of motor-unit firing instances with fewer errors. The performance of the algorithm is governed by a trade-off between the yield of MUAPTs obtained above a given accuracy level and the time required to perform the decomposition. When applied to a set of sEMG signals synthesized from real MUAPTs, the identification error was reduced by an average of 1.78%, improving the accuracy to 97.0%, and the location error was reduced by an average of 1.66 ms. The error-reduction algorithm in this study is not limited to any specific decomposition strategy. Rather, we propose it be used for other decomposition methods, especially when analyzing precise motor-unit firing instances, as occurs when measuring synchronization.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.