Experimental and modeling investigation of spectral compression of biceps brachii SEMG activity with increasing force levels

Gabriel, David A.; Kamen, Gary

doi:10.1016/j.jelekin.2007.10.009

Cited by 40 publications

(26 citation statements)

References 50 publications

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“…The RMS amplitude of the surface EMG signal showed a cubic increase from 0.075 § 0.04 mV to 1.04 § 0.32 mV with increasing force, while the MPF followed a quadratic trend, increasing from 117.5 § 39.2 to 158.8 § 28.9 Hz, followed by a slight decrease to 134.1 § 27.3 Hz. These amplitude and frequency values of surface EMG are also in line with the previous studies reporting sEMG amplitude ranging from 0.075 to 1.05 mV, and MPF values ranging from 89 to 123 Hz in the biceps brachii (Gabriel and Kamen 2009;Moritani and Muro 1987).…”

Section: Discussionsupporting

confidence: 92%

Relationships between surface EMG variables and motor unit firing rates

et al. 2009

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Although surface electromyography (sEMG) is a widely used electrophysiological technique, its physiological interpretation remains somewhat controversial. This study examined the relationship between motor unit firing rates (MUFR) and the root mean square (RMS) amplitude and mean power frequency (MPF) of the sEMG signal in the biceps brachii. Eleven subjects performed maximal isometric elbow flexion while indwelling and sEMG recordings were obtained from the biceps. The RMS amplitude and MPF of the surface signal, and the mean MUFR from the indwelling signal, were calculated over 500 ms epochs. Group means showed a strong MUFR-RMS amplitude relationship (r (2) = 0.91), but a weak MUFR-MPF relationship (r (2) = 0.20). Using all trials, the MUFR-RMS amplitude (r (2) = 0.19) and MUFR-MPF (r (2) = 0.0037) relationships were much weaker. Within individual subjects, the MUFR-RMS amplitude (mean r (2) = 0.13 +/- 0.17) and the MUFR-MPF (mean r (2) = 0.040 +/- 0.041) relationships were also weak. These results suggest that MUFR cannot be predicted from the characteristics of the sEMG signal.

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Section: Discussionsupporting

confidence: 92%

Relationships between surface EMG variables and motor unit firing rates

et al. 2009

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“…Second, the power spectrum of the EMG signal depend on anatomical properties of the muscle fibers, such as length, position of the end plate, and fiber inclination (5), and on the profile of the intracellular action potential (6). Third, the spectral properties of the surface EMG also depend on the distribution of discharge rates of the active motor units and on their degree of synchronization (10,30). These reasons indicate that the surface EMG power spectrum is influenced by factors other than the average conduction velocity of the action potentials and that these factors are independent of the method used to estimate the power spectrum.…”

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confidence: 99%

Counterpoint: Spectral properties of the surface emg do not provide information about motor unit recruitment and muscle fiber type

Farina

2008

Journal of Applied Physiology

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The surface electromyogram (EMG) comprises the sum of the electrical contributions made by the active motor units to the interference signal detected by electrodes placed on the skin overlying the muscle. Because it provides a global measure of motor unit activity, this signal is a valuable tool for assessing the level of muscle activation.The analysis of the surface EMG in the frequency domain has been used in some instances, such as fatiguing contractions, to provide an indirect measure of the relative changes in average muscle fiber conduction velocity (19) or in the profile of the intracellular action potential (6). In addition to these applications, the spectral characteristics of the surface EMG have been used to infer motor unit recruitment strategies and the fiber-type composition of a muscle. Higher characteristic spectral frequencies are associated with a greater proportion of type II fibers (20, 21) and changes in EMG spectral variables are used to infer the recruitment of faster or slower motor units (3,26).The capacity of surface EMG spectral properties to provide information about motor unit recruitment or the proportion of fiber types is based on the rationale that higher threshold (and type II) motor units produce surface action potentials with larger relative energy at higher frequencies than lower threshold (and type I) motor units.Because in these applications the main determinant of the frequency content of an action potential is assumed to be its conduction velocity (28), the crucial issue in this debate is the validity of two assumptions: 1) average conduction velocity of the active motor units is related to fiber-type proportions, and 2) changes in the spectral properties of the surface EMG are associated with changes in average conduction velocity.Fiber-type composition and average muscle fiber conduction velocity. There are several physiological details that confound this association. First, the two main fiber types do not have distinct conduction velocities in humans, but rather conduction velocity has a continuous distribution with a single peak (25). Second, average conduction velocity of muscle fiber action potentials can differ among populations of motor units due to differences in fiber diameter and independent of changes in fiber-type proportions (4). Third, the number of muscle fibers innervated by a motor unit has a skewed distribution. For example, the first dorsal interosseus muscle comprises an equal number of type I and II fibers, but ϳ84% of the motor units have slow contraction times and are fatigue resistant (7). Fourth, the conduction velocity of muscle fiber action potentials can change by ϳ20% with variation in discharge rate (24) and the range of discharge rates varies with recruitment threshold in a population of motor units (2). Fifth, the conduction velocity of muscle fiber action potentials varies with fiber length (15).Experimental evidence: fiber-type composition vs. muscle fiber conduction velocity. The significance of the physiological limitations discussed in...

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“…Intracellular action potential (IAP) was adopted as the starting point of this sEMG model. IAP is mathemati- MU size (D MU ) [19] 12 mm Muscle fiber lengths L 0.5f (right+left) [17] 70 mm, mean=0, SD=1, Range=±2 mm Fiber-endplate position [18] Mean=0, Range=±3 mm Fiber-tendon distance [17] Range=±6 mm Fiber position [16] Range ±1 MU radius MU position [16] Range ±1 muscle radius cally formulated as [21] 3 ( ) 768 90,…”

Section: Intracellular Action Potential Simulationmentioning

confidence: 99%

A simulation study on the relation between muscle motor unit numbers and the non-Gaussianity/non-linearity levels of surface electromyography

Yan

2012

Sci. China Life Sci.

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Recent research has demonstrated that surface electromyography (sEMG) signals have non-Gaussianity and non-linearity properties. It is known that more muscle motor units are recruited and firing rates (FRs) increase as exertion increases. A hypothesis was proposed that the Gaussianity test (S g ) and linearity test (S ℓ ) levels of sEMG signals are associated with the number of active motor units (nMUs) and the FR. The hypothesis has only been preliminarily discussed in experimental studies. We used a simulation sEMG model involving spatial (active MUs) and temporal (three FRs) information to test the hypothesis. Higher-order statistics (HOS) from the bi-frequency domain were used to perform S g and S ℓ . Multivariate covariance analysis and a correlation test were employed to determine the nMUs-Sg relationship and the nMUs-S ℓ relationship. Results showed that nMUs, the FR, and the interaction of nMUs and the FR all influenced the Sg and S ℓ values. The nMUs negatively correlated to both the Sg and S ℓ values. That is, at the three FRs, sEMG signals tended to a more Gaussian and linear distribution as exertion and nMUs increased. The study limited experiment factors to the sEMG non-Gaussianity and non-linearity levels. The study quantitatively described nMUs and the FR of muscle that are not directly available from experiments. Our finding has guiding significance for muscle capability assessment and prosthetic control. simulation surface electromyography model, higher-order statistics, motor unit, firing rate Citation:Zhao Y, Li D X. A simulation study on the relation between muscle motor unit numbers and the non-Gaussianity/non-linearity levels of surface electromyography. Sci China Life Sci, 2012, 55: 958 -967,

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Experimental and modeling investigation of spectral compression of biceps brachii SEMG activity with increasing force levels

Cited by 40 publications

References 50 publications

Relationships between surface EMG variables and motor unit firing rates

Relationships between surface EMG variables and motor unit firing rates

Counterpoint: Spectral properties of the surface emg do not provide information about motor unit recruitment and muscle fiber type

A simulation study on the relation between muscle motor unit numbers and the non-Gaussianity/non-linearity levels of surface electromyography

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