Surface EMG models: properties and applications

Stegeman, Dick F.; Blok, Joleen H.; Hermens, Hermie; Roeleveld, Karin

doi:10.1016/s1050-6411(00)00023-7

Cited by 241 publications

(226 citation statements)

References 73 publications

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“…A c c e p t e d m a n u s c r i p t -6 -However, under realistic conditions many motor units are simultaneously active, and the contributions of the individual motor units can barely be recognized in the recorded signal, but are intermingled in an interference pattern (Stegeman et al, 2000). This signal represents the surface electromyogram (sEMG).…”

Section: Motor Unit Action Potential and Electromyogrammentioning

confidence: 99%

Modeling the vestibular evoked myogenic potential

Lütkenhöner¹,

Stoll²,

Basel³

2010

Journal of Theoretical Biology

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Measuring the vestibular evoked myogenic potential (VEMP) promises to become a routine method for assessing vestibular function, although the technique is not yet standardized. To overcome the problem that the VEMP amplitude depends not only on the inhibition triggered by the acoustic stimulation of the vestibular end organs in the inner ear, but also on the tone of the muscle from which the potential is recorded, the VEMP is often normalized by dividing through a measure of the electromyogram (EMG) activity. The underlying idea is that VEMP amplitude and EMG activity are proportional. But this would imply that the muscle tone is irrelevant for a successful VEMP recording, contradicting experimental evidence. Here, an analytical model is presented that allows to resolve the contradiction. The EMG is modeled as the sum of motor unit action potentials (MUAPs). A brief inhibition can be characterized by its equivalent rectangular duration (ERD), irrespective of the actual time course of the inhibition. The VEMP resembles a polarity-inverted MUAP under such circumstances. Its amplitude is proportional to both the ERD and the MUAP rate. The EMG activity, by contrast, is proportional to the square root of the MUAP rate. Thus, the normalized VEMP still depends on the muscle tone. To avoid confounding effects of the muscle tone, the standard deviation of the EMG could be considered. But the inhibition effect on the standard deviation is small so that the measuring time would have to be much longer than usual today.

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Section: Motor Unit Action Potential and Electromyogrammentioning

confidence: 99%

Modeling the vestibular evoked myogenic potential

Lütkenhöner¹,

Stoll²,

Basel³

2010

Journal of Theoretical Biology

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“…FMG, after the processing used herein, is rather analogous to the average rectified values of surface EMG and the RMS-based EMG processing described by Basmajian and De Luca [32]. Surface EMG may be considered a structure-based model, reductionally representing the system's important elements [33], whereas FMG is a more phenomenological model, based on its direct correlations and resemblance to grip force.…”

Section: Force Myography As Control Modalitymentioning

confidence: 99%

Pressure signature of forearm as predictor of grip force

Wininger

Kim

Craelius

2008

JRRD

113

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Abstract-We studied the relationship between grip force and external forearm pressure in nondisabled subjects using force myography (FMG). FMG uses a sensorized cuff surrounding the forearm to register the distributed mechanical force, detecting pressure on the sensors generated by the volumetric changes of the underlying musculo-tendinous complex. Each of nine nondisabled subjects donned the FMG cuff and applied grip forces to a cylindrical dynamometer; grip forces ranged from 0% to 100% of the subjects' maximum voluntary contraction. The cuff was positioned with seven force sensors located on both the anterior and posterior surfaces of the proximal forearm, but no attempt was made to match sensor placement with particular muscles or sites. Grip prediction was simply encoded as the rectified sum of the FMG sensor outputs. During grip and release cycles, the FMG waveforms of each subject correlated closely with his or her force waveforms (r > 0.89). FMG also correlated highly with the timing of grip onset and release (intraclass correlation coefficient (ICC(A,2)) = 0.99) and time to peak (ICC(A,2) = 0.91), with negligible lag. These results demonstrate that when applied to the forearm, FMG represents a grip force signature that may be useful for near-real-time proportional control of upper-limb prosthetic devices.

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“…Another limitation of the physical model used for the generation of the surface MUAPs is the description of the subcutaneous layers and of the muscle as infinite parallel layers (13,15). Other models may be better in this respect, as, for example, the approach followed by Kleine et al (25), who assumed a cylindrical volume conductor, which probably simulates the arm better (40). Again, we do not think our main conclusions would be different by assuming a different volume conductor shape.…”

Section: Simulation Of the Muapsmentioning

confidence: 99%

Nonlinear surface EMG analysis to detect changes of motor unit conduction velocity and synchronization

Farina

Fattorini

Felici

et al. 2002

Journal of Applied Physiology

170

142

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. Nonlinear surface EMG analysis to detect changes of motor unit conduction velocity and synchronization. J Appl Physiol 93: 1753-1763. First published August 9, 2002 10.1152/japplphysiol.00314.2002Amplitude and frequency content of the surface electromyographic (EMG) signal reflect central and peripheral modifications of the neuromuscular system. Classic surface EMG spectral variables applied to assess muscle functions are the centroid and median power spectral frequencies. More recently, nonlinear tools have been introduced to analyze the surface EMG; among them, the recurrence quantification analysis (RQA) was shown to be particularly promising for the detection of muscle status changes. The purpose of this work was to analyze the effect of motor unit short-term synchronization and conduction velocity (CV) on EMG spectral variables and two variables extracted by RQA, the percentage of recurrence (%Rec) and determinism (%Det). The study was performed on the basis of a simulation model, which allowed changing the degree of synchronization and mean CV of a number of motor units, and of an experimental investigation of the surface EMG signal properties detected during high-force-level isometric fatiguing contractions of the biceps brachii muscle. Simulations and experimental results were largely in agreement and show that 1) spectral variables, %Rec, and %Det are influenced by CV and degree of synchronization; 2) spectral variables are highly correlated with %Det (R ϭ Ϫ0.95 in the simulations and Ϫ0.78 and Ϫ0.75 for the initial values and normalized slopes, respectively, in the experimental signals), and thus the information they provide on muscle properties is basically the same; and 3) variations of %Det and %Rec in response to changes in muscle properties are significantly larger than the variations of spectral variables. This study validates RQA as a means for fatigue assessment with potential advantages (such as the higher sensitivity to changes of muscle status) with respect to the classic spectral analysis. surface electromyography; motor unit short-term synchronization; surface electromyographic modeling; recurrence plot analysis THE SURFACE ELECTROMYOGRAPHIC (EMG) signal presents smaller bandwidth with respect to the intramuscular EMG, because the tissues separating the muscle fibers and the recording electrodes act as low-pass filters (15,28). This determines low-spatial selectivity, which hinders the separation of the contributions of different motor units (MUs). For this reason, past research efforts in the surface EMG field were mainly devoted to the development of processing techniques in time and frequency domain, which gave indications about the global EMG activity, without aiming at an analysis at the single MU level. Although, recently, techniques for noninvasive assessment of single MU properties have been proposed and refined (4,6,8), their applicability is still limited by the need of complex detection systems, to low-contraction levels, and controlled laboratory conditions for signal recordi...

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Surface EMG models: properties and applications

Cited by 241 publications

References 73 publications

Modeling the vestibular evoked myogenic potential

Modeling the vestibular evoked myogenic potential

Pressure signature of forearm as predictor of grip force

Nonlinear surface EMG analysis to detect changes of motor unit conduction velocity and synchronization

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