scholarly journals Error reduction in EMG signal decomposition

2014 ◽  
Vol 112 (11) ◽  
pp. 2718-2728 ◽  
Author(s):  
Joshua C. Kline ◽  
Carlo J. De Luca
Keyword(s):  
Motor Unit ◽  
Decomposition Methods ◽  
Surface Emg ◽  
Error Reduction ◽  
Motor Unit Action Potential ◽  
Reduction Algorithm ◽  
Emg Signal ◽  
Motor Unit Firing ◽  
Time Required ◽  
Semg Signals

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.

scholarly journals Multi-Channel Surface EMG Spatio-Temporal Image Enhancement Using Multi-Scale Hessian-Based Filters

2020 ◽  
Vol 10 (15) ◽  
pp. 5099 ◽  
Author(s):  
Khalil Ullah ◽  
Khalil Khan ◽  
Muhammad Amin ◽  
Muhammad Attique ◽  
Tae-Sun Chung ◽  
...  
Keyword(s):  
Action Potential ◽  
Motor Unit ◽  
Linear Structure ◽  
Surface Emg ◽  
Motor Unit Action Potential ◽  
Multi Scale ◽  
Motor Unit Action ◽  
Spatio Temporal ◽  
Unit Action ◽  
Semg Signals

Surface electromyography (sEMG) signals acquired with linear electrode array are useful in analyzing muscle anatomy and physiology. Most algorithms for signal processing, detection, and estimation require adequate quality of the input signals, however, multi-channel sEMG signals are commonly contaminated due to several noise sources. The sEMG signal needs to be enhanced prior to the digital signal and image processing to achieve the best results. This study is using spatio-temporal images to represent surface EMG signals. The motor unit action potential (MUAP) in these images looks like a linear structure, making certain angles with the x-axis, depending on the conduction velocity of the MU. A multi-scale Hessian-based filter is used to enhance the linear structure, i.e., the MUAP region, and to suppress the background noise. The proposed framework is compared with some of the existing algorithms using synthetic, simulated, and experimental sEMG signals. Results show improved detection accuracy of the motor unit action potential after the proposed enhancement as a preprocessing step.

scholarly journals Two-Source Validation of Progressive FastICA Peel-Off for Automatic Surface EMG Decomposition in Human First Dorsal Interosseous Muscle

2018 ◽  
Vol 28 (09) ◽  
pp. 1850019 ◽  
Author(s):  
Maoqi Chen ◽  
Xu Zhang ◽  
Zhiyuan Lu ◽  
Xiaoyan Li ◽  
Ping Zhou
Keyword(s):  
Motor Unit ◽  
Electrode Array ◽  
Surface Emg ◽  
High Density ◽  
Motor Unit Action Potential ◽  
First Dorsal Interosseous ◽  
First Dorsal Interosseous Muscle ◽  
Density Surface ◽  
Motor Unit Action ◽  
Semg Signals

This study aims to assess the accuracy of a novel high density surface electromyogram (SEMG) decomposition method, namely automatic progressive FastICA peel-off (APFP), for automatic decomposition of experimental electrode array SEMG signals. A two-source method was performed by simultaneous concentric needle EMG and electrode array SEMG recordings from the human first dorsal interosseous (FDI) muscle, using a protocol commonly applied in clinical EMG examination. The electrode array SEMG was automatically decomposed by the APFP while the motor unit action potential (MUAP) trains were also independently identified from the concentric needle EMG. The degree of agreement of the common motor unit (MU) discharge timings decomposed from the two different categories of EMG signals was assessed. A total of 861 and 217 MUs were identified from the 114 trials of simultaneous high density SEMG and concentric needle EMG recordings, respectively. Among them 168 common (MUs) were found with a high average matching rate of [Formula: see text] for the discharge timings. The outcomes of this study show that the APFP can reliably decompose at least a subset of MUs in the high density SEMG signals recorded from the human FDI muscle during low contraction levels using a protocol analog to clinical EMG examination.

Amplitude cancellation reduces the size of motor unit potentials averaged from the surface EMG

2006 ◽  
Vol 100 (6) ◽  
pp. 1928-1937 ◽  
Author(s):  
Kevin G. Keenan ◽  
Dario Farina ◽  
Roberto Merletti ◽  
Roger M. Enoka
Keyword(s):  
Motor Unit ◽  
Conduction Velocity ◽  
Motor Units ◽  
Surface Emg ◽  
Excitation Level ◽  
Bipolar Electrode ◽  
Emg Signal ◽  
Motor Unit Synchronization ◽  
Rate Coding ◽  
Simulated Surface

The purpose of the study was to evaluate the influence of selected physiological parameters on amplitude cancellation in the simulated surface electromyogram (EMG) and the consequences for spike-triggered averages of motor unit potentials derived from the interference and rectified EMG signals. The surface EMG was simulated from prescribed recruitment and rate coding characteristics of a motor unit population. The potentials of the motor units were detected on the skin over a hand muscle with a bipolar electrode configuration. Averages derived from the EMG signal were generated using the discharge times for each of the 24 motor units with lowest recruitment thresholds from a population of 120 across three conditions: 1) excitation level; 2) motor unit conduction velocity; and 3) motor unit synchronization. The area of the surface-detected potential was compared with potentials averaged from the interference, rectified, and no-cancellation EMGs. The no-cancellation EMG comprised motor unit potentials that were rectified before they were summed, thereby preventing cancellation between the opposite phases of the potentials. The percent decrease in area of potentials extracted from the rectified EMG was linearly related to the amount of amplitude cancellation in the interference EMG signal, with the amount of cancellation influenced by variation in excitation level and motor unit conduction velocity. Motor unit synchronization increased potentials derived from both the rectified and interference EMG signals, although cancellation limited the increase in area for both potentials. These findings document the influence of amplitude cancellation on motor unit potentials averaged from the surface EMG and the consequences for using the procedure to characterize motor unit properties.

Rectification is required to extract oscillatory envelope modulation from surface electromyographic signals

2014 ◽  
Vol 112 (7) ◽  
pp. 1685-1691 ◽  
Author(s):  
Christopher J. Dakin ◽  
Brian H. Dalton ◽  
Billy L. Luu ◽  
Jean-Sébastien Blouin
Keyword(s):  
Motor Unit ◽  
Stimulus Frequency ◽  
Motor Units ◽  
Surface Emg ◽  
Medial Gastrocnemius ◽  
Single Motor Unit ◽  
Single Motor ◽  
Single Motor Units ◽  
Emg Signal ◽  
Envelope Modulation

Rectification of surface electromyographic (EMG) recordings prior to their correlation with other signals is a widely used form of preprocessing. Recently this practice has come into question, elevating the subject of EMG rectification to a topic of much debate. Proponents for rectifying suggest it accentuates the EMG spike timing information, whereas opponents indicate it is unnecessary and its nonlinear distortion of data is potentially destructive. Here we examine the necessity of rectification on the extraction of muscle responses, but for the first time using a known oscillatory input to the muscle in the form of electrical vestibular stimulation. Participants were exposed to sinusoidal vestibular stimuli while surface and intramuscular EMG were recorded from the left medial gastrocnemius. We compared the unrectified and rectified surface EMG to single motor units to determine which method best identified stimulus-EMG coherence and phase at the single-motor unit level. Surface EMG modulation at the stimulus frequency was obvious in the unrectified surface EMG. However, this modulation was not identified by the fast Fourier transform, and therefore stimulus coherence with the unrectified EMG signal failed to capture this covariance. Both the rectified surface EMG and single motor units displayed significant coherence over the entire stimulus bandwidth (1–20 Hz). Furthermore, the stimulus-phase relationship for the rectified EMG and motor units shared a moderate correlation ( r = 0.56). These data indicate that rectification of surface EMG is a necessary step to extract EMG envelope modulation due to motor unit entrainment to a known stimulus.

scholarly journals The effects of vibration-induced altered stretch reflex sensitivity on maximal motor unit firing properties

2019 ◽  
Vol 121 (6) ◽  
pp. 2215-2221 ◽  
Author(s):  
Alejandra Barrera-Curiel ◽  
Ryan J. Colquhoun ◽  
Jesus A. Hernandez-Sarabia ◽  
Jason M. DeFreitas
Keyword(s):  
Motor Unit ◽  
Muscle Spindle ◽  
Stretch Reflex ◽  
Muscle Spindles ◽  
Motor Unit Action Potential ◽  
Firing Rates ◽  
Motor Unit Firing ◽  
Firing Properties ◽  
Spindle Function ◽  
Voluntary Strength

It is well known that muscle spindles have a monosynaptic, excitatory connection with α-motoneurons. However, the influence of muscle spindles on human motor unit behavior during maximal efforts remains untested. It has also been shown that muscle spindle function, as assessed by peripheral reflexes, can be systematically manipulated with muscle vibration. Therefore, the purpose of this study was to analyze the effects of brief and prolonged vibration on maximal motor unit firing properties. A crossover design was used, in which each of the 24 participants performed one to three maximal knee extensions under three separate conditions: 1) control, 2) brief vibration that was applied during the contraction, and 3) after prolonged vibration that was applied for ~20 min before the contraction. Multichannel EMG was recorded from the vastus lateralis during each contraction and was decomposed into its constituent motor unit action potential trains. Surprisingly, an approximate 9% reduction in maximal voluntary strength was observed not only after prolonged vibration but also during brief vibration. In addition, both vibration conditions had a large, significant effect on firing rates (a decrease in the rates) and a small to moderate, nonsignificant effect on recruitment thresholds (a small increase in the thresholds). Therefore, vibration had a detrimental influence on both maximal voluntary strength and motor unit firing properties, which we propose is due to altered function of the stretch reflex pathway. NEW & NOTEWORTHY We used vibration to alter muscle spindle function and examined the vibration’s influence on maximal motor unit properties. We discovered that vibration had a detrimental influence on motor unit behavior and motor output by decreasing motor unit firing rates, increasing recruitment thresholds, which led to decreased maximal strength. We believe that understanding the role of muscle spindles during maximal contractions provides a deeper insight into motor control and sensorimotor integration.

scholarly journals The extraction of neural strategies from the surface EMG: an update

2014 ◽  
Vol 117 (11) ◽  
pp. 1215-1230 ◽  
Author(s):  
Dario Farina ◽  
Roberto Merletti ◽  
Roger M. Enoka
Keyword(s):  
Motor Unit ◽  
Action Potentials ◽  
Surface Emg ◽  
Neural Code ◽  
Neural Activation ◽  
Emg Signal ◽  
Motor Unit Action Potentials ◽  
Motor Unit Action ◽  
Unit Action ◽  
Surface Emg Signal

A surface EMG signal represents the linear transformation of motor neuron discharge times by the compound action potentials of the innervated muscle fibers and is often used as a source of information about neural activation of muscle. However, retrieving the embedded neural code from a surface EMG signal is extremely challenging. Most studies use indirect approaches in which selected features of the signal are interpreted as indicating certain characteristics of the neural code. These indirect associations are constrained by limitations that have been detailed previously (Farina D, Merletti R, Enoka RM. J Appl Physiol 96: 1486–1495, 2004) and are generally difficult to overcome. In an update on these issues, the current review extends the discussion to EMG-based coherence methods for assessing neural connectivity. We focus first on EMG amplitude cancellation, which intrinsically limits the association between EMG amplitude and the intensity of the neural activation and then discuss the limitations of coherence methods (EEG-EMG, EMG-EMG) as a way to assess the strength of the transmission of synaptic inputs into trains of motor unit action potentials. The debated influence of rectification on EMG spectral analysis and coherence measures is also discussed. Alternatively, there have been a number of attempts to identify the neural information directly by decomposing surface EMG signals into the discharge times of motor unit action potentials. The application of this approach is extremely powerful, but validation remains a central issue.

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