This brief review examines some of the methods used to infer central control strategies from surface electromyogram (EMG) recordings. Among the many uses of the surface EMG in studying the neural control of movement, the review critically evaluates only some of the applications. The focus is on the relations between global features of the surface EMG and the underlying physiological processes. Because direct measurements of motor unit activation are not available and many factors can influence the signal, these relations are frequently misinterpreted. These errors are compounded by the counterintuitive effects that some system parameters can have on the EMG signal. The phenomenon of crosstalk is used as an example of these problems. The review describes the limitations of techniques used to infer the level of muscle activation, the type of motor unit recruited, the upper limit of motor unit recruitment, the average discharge rate, and the degree of synchronization between motor units. Although the global surface EMG is a useful measure of muscle activation and assessment, there are limits to the information that can be extracted from this signal.

The extraction of neural strategies from the surface EMG

Advances in materials, mechanics, and manufacturing now allow construction of high-quality electronics and optoelectronics in forms that can readily integrate with the soft, curvilinear, and time-dynamic surfaces of the human body. The resulting capabilities create new opportunities for studying disease states, improving surgical procedures, monitoring health/wellness, establishing human-machine interfaces, and performing other functions. This review summarizes these technologies and illustrates their use in forms integrated with the brain, the heart, and the skin.

https://www.annualreviews.org/doi/pdf/10.1146/annurev-bioeng-071811-150018

Flexible and Stretchable Electronics for Biointegrated Devices

Electromyography (EMG) signals are becoming increasingly important in many applications, including clinical/biomedical, prosthesis or rehabilitation devices, human machine interactions, and more. However, noisy EMG signals are the major hurdles to be overcome in order to achieve improved performance in the above applications. Detection, processing and classification analysis in electromyography (EMG) is very desirable because it allows a more standardized and precise evaluation of the neurophysiological, rehabitational and assistive technological findings. This paper reviews two prominent areas; first: the pre-processing method for eliminating possible artifacts via appropriate preparation at the time of recording EMG signals, and second: a brief explanation of the different methods for processing and classifying EMG signals. This study then compares the numerous methods of analyzing EMG signals, in terms of their performance. The crux of this paper is to review the most recent developments and research studies related to the issues mentioned above.

/pdf/surface-electromyography-signal-processing-and-138ym9ds1t.pdf

Surface Electromyography Signal Processing and Classification Techniques

Enoka, Roger M. Eccentric contractions require unique activation strategies by the nervous system. J. Appl. Physiol. 81(6): 2339–2346, 1996.—Eccentric contractions occur when activated muscles are ...

Eccentric contractions require unique activation strategies by the nervous system

ROBERTSON, R. J., F. L. GOSS, J. RUTKOWSKI, B. LENZ, C. DIXON, J. TIMMER, K. FRAZEE, J. DUBE, and J. ANDREACCI. Concurrent Validation of the OMNI Perceived Exertion Scale for Resistance Exercise. Med. Sci. Sports Exerc., Vol. 35, No. 2, pp. 333–341, 2003.PurposeConcurrent validity of the newly devel

Concurrent validation of the OMNI perceived exertion scale for resistance exercise

Mean frequency and signal amplitude of the surface EMG of the quadriceps muscles increase with increasing torque - A study using the continuous wavelet transform

Criterion validation of surface EMG variables as fatigue indicators using peak torque: a study of repetitive maximum isokinetic knee extensions.

This study is an investigation of the relationship between muscle morphology and surface electromyographic (EMG) parameters [mean frequency of the power spectrum (MNF), signal amplitude (root mean square, RMS) and the signal amplitude ratio (SAR; i.e. the ratio between the RMS level during the passive part of the contraction cycle and the RMS level during the active part of the contraction cycle)] during 100 maximal dynamic knee extensions at 90° · s−1. Each contraction cycle comprised of 1 s of active knee extension and 1 s of passive knee flexion. The surface EMG was recorded from the vastus lateralis muscle. Twenty clinically healthy subjects participated in the study, and muscle biopsy samples of the vastus lateralis were obtained from 19 of those subjects. The relationships between muscle morphology and EMG were investigated at three stages of the test: initially, during the fatigue phase (initial 40 contractions), and at the endurance level (the final 50 contractions). Major findings on correlations are that SAR and MNF tended to correlate positively with the proportion of type 1 fibres, and RMS correlated positively with the proportion of type 2 muscle fibres. The muscle fibre areas showed little correlation with the EMG variables under investigation. The results of the present study showed that the three EMG variables of a dynamic endurance test that were investigated (RMS, MNF and SAR) were clearly correlated with the proportions of the different fibre types, but only to a small extent with fibre areas. These findings contradict some of the theoretical models of the EMG, especially for parameters in the frequency domain.

The influences of muscle fibre proportions and areas upon EMG during maximal dynamic knee extensions.

This study investigated changes in intramuscular pressure (IMP) and surface electromyographic (EMG) parameters (mean frequency of the power spectrum, fmean; and signal amplitude denoted as root mean square, RMS) during contractions to fatigue at 25 and 70% of maximal voluntary contraction (MVC). Parameters were recorded simultaneously from the vastus lateralis muscle during knee extension. A significant decrease in fmean occurred with time at both contraction levels; however, the rate of decline (slope) was greater at 70% MVC. RMS increased throughout the contractions at both levels, with the relative increase being significantly greater for 25% MVC. IMP increased during 25% MVC but did not change during the 70% MVC contraction. IMP at rest was significantly higher post-contractions than it was pre-contractions at 25% MVC (21.1 vs. 8.0 mmHg, P < 0.01) and 70% MVC (13.7 vs. 8.6 mmHg, P < 0.01). Consequently, post-contraction IMP was higher at 25% MVC than at 70% MVC (P < 0.01). IMP changes throughout the fatiguing contractions correlated negatively with fmean and positively with RMS at both MVC levels; however, these correlations were better at 25% MVC. The extent of intramuscular water accumulation is discussed as a major cause of the difference in IMP changes between 25% and 70% MVC. Significant differences in the rate of change for all parameters between high vs. low contraction levels may suggest a common mechanism governing changes in IMP and EMG fatigue indicators.

Differential responses in intramuscular pressure and EMG fatigue indicators during low‐ vs. high‐level isometric contractions to fatigue

This study investigated the relationship between muscle morphology and surface electromyographic parameters (mean frequency, f(mean); and signal amplitude, RMS) during sustained static knee extension to exhaustion at 25% maximal voluntary contraction (MVC) and at 70% MVC. Twenty clinically healthy subjects participated. EMGs were registered from the quadriceps. Muscle forces during knee extension at a 90 degree joint angle were maintained at the respective levels throughout the measurement periods. A biopsy was obtained of the vastus lateralis muscle. The total time to exhaustion was normalized for each subject. By means of regression analysis, the intercept (i) (i.e. the unfatigued state) and the regression coefficient (k) were determined for each EMG parameter. The endurance time increased with decreasing force level. A significantly higher perception of fatigue was found at 25% MVC than at 70% MVC. From principal component analyses it was concluded that RMS-k at 25% MVC mainly correlated with the type 2 muscle fibre proportions (%), and at 70% MVC mainly with the areas of type 2 muscle fibre. At 25% MVC, f(mean)-k correlated with the areas of type 2A, 2B and 2C fibres, and at 70% MVC negatively with the proportion of type 2B and to some extent with areas of type 2A, 2B and 2C fibres. f(mean)-i at 25% MVC correlated with fibre type proportions (%); f(mean)-i at 70% MVC correlated with the areas of type 2A, 2B and 2C. The present study indicates relationships between surface EMG and muscle morphology, which is contrary to presented models of the EMG.

S. Karlsson

Papers

Mean frequency and signal amplitude of the surface EMG of the quadriceps muscles increase with increasing torque - A study using the continuous wavelet transform

Criterion validation of surface EMG variables as fatigue indicators using peak torque: a study of repetitive maximum isokinetic knee extensions.

The influences of muscle fibre proportions and areas upon EMG during maximal dynamic knee extensions.

Differential responses in intramuscular pressure and EMG fatigue indicators during low‐ vs. high‐level isometric contractions to fatigue

The relationships between EMG and muscle morphology throughout sustained static knee extension at two submaximal force levels