Elimination of electrocardiogram contamination from electromyogram signals: An evaluation of currently used removal techniques.

Trunk electromyographic signals (EMG) are often contaminated with heart muscle electrical activity (ECG) due to the proximity of the collection sites to the heart and the volume conduction characteristics of the ECG through the torso. Few studies have quantified ECG removal techniques relative to an uncontaminated EMG signal (gold standard or criterion measure), or made direct comparisons between different methods for a given set of data. Understanding the impacts of both untreated contaminated EMG and ECG elimination techniques on the amplitude and frequency parameters is vital given the widespread use of EMG. The purpose of this study was to evaluate four groups of current and commonly used techniques for the removal of ECG contamination from EMG signals. ECG recordings at two intensity levels (rest and 50% maximum predicted heart rate) were superimposed on 11 uncontaminated biceps brachii EMG signals (rest, 7 isometric and 3 isoinertial levels). The 23 removal methods used were high pass digital filtering (finite impulse response (FIR) using a Hamming window, and fourth-order Butterworth (BW) filter) at five cutoff frequencies (20, 30, 40, 50, and 60 Hz), template techniques (template subtraction and an amplitude gating template), combinations of the subtraction template and high pass digital filtering, and a frequency subtraction/signal reconstruction method. For muscle activation levels between 10% and 25% of maximum voluntary contraction, the template subtraction and BW filter with a 30 Hz cutoff were the two best methods for maximal ECG removal with minimal EMG distortion. The BW filter with a 30 Hz cutoff provided the optimal balance between ease of implementation, time investment, and performance across all contractions and heart rate levels for the EMG levels evaluated in this study.     

The interpretation of abdominal wall muscle recruitment strategies change when the electrocardiogram (ECG) is removed from the electromyogram (EMG)

The purpose of this study was to determine the effect of the ECG artifact on low-level trunk muscle activation amplitudes and assess the effectiveness of two methods used to remove the ECG. Simulations were performed and percent error in root mean square (RMS) amplitudes were calculated from uncontaminated and contaminated EMG signals at various ECG to EMG ratios. Two methods were used to remove the ECG: (1) filtering by adaptive sampling (FAS) and (2) Butterworth high pass filter at 30 Hz (BW-30 Hz HPF). The percent error was also calculated between the ECG removed and the uncontaminated EMG RMS amplitudes. Next, the BW-30 Hz HPF method was used to remove the ECG from 3-bilateral external oblique (EO) muscle sites collected from 30 healthy subjects performing a one handed lift and replace task. Two separate ANOVA models assessed the effects of ECG on the statistical interpretation of EO recruitment strategies. One model included EMG data that contained the ECG and the other model included EMG data after the ECG was removed. Large percent errors were observed when the ECG was not removed. These errors increased with larger ECG to EMG ratios. Both removal methods reduced the errors to below 10%, but the BW-30 Hz HPF method was more time efficient in removing the ECG artifact. Different statistical findings were observed among the muscle sites for the ECG contaminated model compared to the ECG removed model, which resulted in different conclusions concerning neuromuscular control.     

Effects of electrocardiography contamination and comparison of ECG removal methods on upper trapezius electromyography recordings

Electromyography (EMG) recordings from the trapezius are often contaminated by the electrocardiography (ECG) signal, making it difficult to distinguish low-level muscle activity from muscular rest. This study investigates the influence of ECG contamination on EMG amplitude and frequency estimations in the upper trapezius during muscular rest and low-level contractions. A new method of ECG contamination removal, filtered template subtraction (FTS), is described and compared to 30 Hz high-pass filter (HPF) and averaged template subtraction (ATS) methods. FTS creates a unique template of each ECG artifact using a low-pass filtered copy of the contaminated signal, which is subtracted from contaminated periods in the original signal. ECG contamination results in an over-estimation of EMG amplitude during rest in the upper trapezius, with negligible effects on amplitude and frequency estimations during low-intensity isometric contractions. FTS and HPF successfully removed ECG contamination from periods of muscular rest, yet introduced errors during muscle contraction. Conversely, ATS failed to fully remove ECG contamination during muscular rest, yet did not introduce errors during muscle contraction. The relative advantages and disadvantages of different ECG contamination removal methods should be considered in the context of the specific motor tasks that require analysis.     

Adaptive filtering for ECG rejection from surface EMG recordings.

Surface electromyograms (EMG) of back muscles are often corrupted by electrocardiogram (ECG) signals. This noise in the EMG signals does not allow to appreciate correctly the spectral content of the EMG signals and to follow its evolution during, for example, a fatigue process. Several methods have been proposed to reject the ECG noise from EMG recordings, but seldom taking into account the eventual changes in ECG characteristics during the experiment. In this paper we propose an adaptive filtering algorithm specifically developed for the rejection of the electrocardiogram corrupting surface electromyograms (SEMG). The first step of the study was to choose the ECG electrode position in order to record the ECG with a shape similar to that found in the noised SEMGs. Then, the efficiency of different algorithms were tested on 28 erector spinae SEMG recordings. The best algorithm belongs to the fast recursive least square family (FRLS). More precisely, the best results were obtained with the simplified formulation of a FRLS algorithm. As an application of the adaptive filtering, the paper compares the evolutions of spectral parameters of noised or denoised (after adaptive filtering) surface EMGs recorded on erector spinae muscles during a trunk extension. The fatigue test was analyzed on 16 EMG recordings. After adaptive filtering, mean initial values of energy and of mean power frequency (MPF) were significantly lower and higher respectively. The differences corresponded to the removal of the ECG components. Furthermore, classical fatigue criteria (increase in energy and decrease in MPF values over time during the fatigue test) were better observed on the denoised EMGs. The mean values of the slopes of the energy-time and MPF-time linear relationships differed significantly when established before and after adaptive filtering. These results account for the efficacy of the adaptive filtering method proposed here to denoise electrophysiological signals.     

Influence of amplitude cancellation on the accuracy of determining the onset of muscle activity from the surface electromyogram

The purpose of the study was to quantify the influence of amplitude cancellation on the accuracy of detecting the onset of muscle activity based on an analysis of simulated surface electromyographic (EMG) signals. EMG activity of a generic lower limb muscle was simulated during the stance phase of human gait. Surface EMG signals were generated with and without amplitude cancellation by summing simulated motor unit potentials either before (cancellation EMG) or after (no-cancellation EMG) the potentials had been rectified. The two sets of EMG signals were compared at forces of 30% and 80% of maximum voluntary contraction (MVC) and with various low-pass filter cut-off frequencies. Onset time was determined both visually and by an algorithm that identified when the mean amplitude of the signal within a sliding window exceeded a specified standard deviation (SD) above the baseline mean. Onset error was greater for the no-cancellation conditions when determined automatically and by visual inspection. However, the differences in onset error between the two cancellation conditions appear to be clinically insignificant. Therefore, amplitude cancellation does not appear to limit the ability to detect the onset of muscle activity from the surface EMG.     

Electromyogram power spectrum during dynamic contractions at different intensities of exercise

During dynamic contractions performed on a cycle ergometer, we studied the influence of motor unit (MU) recruitment on the electromyographic (EMG) spectral content by exerting mechanical power of different intensities, which was chosen to remain below the maximal aerobic power (VO2max). The spectral parameters: EMG total power (PEMG), mean (MPF) and median (MED) power frequencies, which are the most representative of the EMG spectral content, were calculated according to the EMG activity of the vastus medialis muscle (VM) and soleus muscle (SOL) of the right leg. For VM and SOL, PEMG increased linearly with exerted power demonstrating an enhancement of MU recruitment. Moreover these relationships were less scattered when exerted power was expressed as a percentage of VO2max. Changes in MPF and MED with varying exercise intensities were different from one subject to another. For a set of subjects, MPF and MED were found to be independent of exerted power. Although VM and SOL muscles are different in fibre type composition, similar results were obtained for both EMG activities. We have concluded that for dynamic contractions performed at different intensities below VO2max, the recruitment of the MU has a poor effect on the EMG spectral content whatever the predominant type of fibre.     

Simulation analysis of interference EMG during fatiguing voluntary contractions. Part II--changes in amplitude and spectral characteristics.

Capabilities of amplitude and spectral methods for information extraction from interference EMG signals were assessed through simulation and preliminary experiment. Muscle was composed of 4 types of motor units (MUs). Different hypotheses on changes in firing frequency of individual MUs, intracellular action potential (IAP) and muscle fibre propagation velocity (MFPV) during fatigue were analyzed. It was found that changes in amplitude characteristics of interference signals (root mean square, RMS, or integrated rectified value, IEMG) detected by intramuscular and surface electrodes differed. RMS and IEMG of surface detected interference signals could increase even under MU firing rate reduction and without MU synchronisation. IAP profile lengthening can affect amplitude characteristics more significantly than MU firing frequency. Thus, an increase of interference EMG amplitude is unreliable to reflect changes in the neural drive. The ratio between EMG amplitude and contraction response can hardly characterise the so-called 'neuromuscular efficiency'. The recently proposed spectral fatigue indices can be used for quantification of interference EMG signals. The indices are practically insensitive to MU firing frequency. IAP profile lengthening and decrease in MFPV enhanced the index value, while recruitment of fast fatigable MUs reduced it. Sensitivity of the indices was higher than that of indices traditionally used.     

EMG signal amplitude normalization technique in stretch-shortening cycle movements

Analysis of functional movements using surface electromyography (EMG) often involves recording both eccentric and concentric muscle activity during a stretch-shorten cycle (SSC). The techniques used for amplitude normalization are varied and are independent of the type of muscle activity involved. The purpose of this study was: (i) to determine the effect of 11 amplitude normalization techniques on the coefficient of variation (CV) during the eccentric and concentric phases of the SSC; and (ii) to establish the effect of the normalization techniques on the EMG signal under variable load and velocity. The EMG signal of the biceps brachii of eight normal subjects was recorded under four SSC conditions and three levels of isometric contraction. The 11 derived normalization values were total rms, mean rms and peak rms (100 ms time constant) for the isometric contractions and the mean rms and peak rms values of the ensemble values for each set of isotonic contractions. Normalization using maximal voluntary isometric contractions (MVIC), irrespective of rms processing (total, mean or peak), demonstrated greater CV above the raw data for both muscle actions. Mean ensemble values and submaximal isometric recordings reduced the CV of concentric data. No amplitude normalization technique reduced the CV for eccentric data under loaded conditions. An ANOVA demonstrated significant (P < 0.01) main effects for load and velocity on concentric raw data and an interaction (P < 0.05) for raw eccentric data. No significant effects were demonstrated for changes in velocity when the data were normalized using mean rms values. The reduction of the CV should not be at the expense of true biological variance and current normalization techniques poorly serve the analysis of eccentric muscle activity during the SSC.     

Sample entropy analysis of surface EMG for improved muscle activity onset detection against spurious background spikes

Voluntary surface electromyogram (EMG) signal is sometimes contaminated by spurious background spikes of both physiological and extrinsic or accidental origins. A novel method of muscle activity onset detection against such spurious spikes was proposed in this study based primarily on the sample entropy (SampEn) analysis of the surface EMG. The method takes advantage of the nonlinear properties of the SampEn analysis to distinguish voluntary surface EMG signals from spurious background spikes in the complexity domain. To facilitate muscle activity onset detection, the SampEn analysis of surface EMG was first performed to highlight voluntary EMG activity while suppressing spurious background spikes. Then, a SampEn threshold was optimized for muscle activity onset detection. The performance of the proposed method was examined using both semi-synthetic and experimental surface EMG signals. The SampEn based methods effectively reduced the detection error induced by spurious background spikes and achieved improved performance over the methods relying on conventional amplitude thresholding or its extended version in the Teager Kaiser Energy domain.     

Intramuscular and surface electromyogram changes during muscle fatigue

Twelve male subjects were tested to determine the effects of motor unit (MU) recruitment and firing frequency on the surface electromyogram (EMG) frequency power spectra during sustained maximal voluntary contraction (MVC) and 50% MVC of the biceps brachii muscle. Both the intramuscular MU spikes and surface EMG were recorded simultaneously and analyzed by means of a computer-aided intramuscular spike amplitude-frequency histogram and frequency power spectral analysis, respectively. Results indicated that both mean power frequency (MPF) and amplitude (rmsEMG) of the surface EMG fell significantly (P less than 0.001) together with a progressive reduction in MU spike amplitude and firing frequency during sustained MVC. During 50% MVC there was a significant decline in MPF (P less than 0.001), but this decline was accompanied by a significant increase in rmsEMG (P less than 0.001) and a progressive MU recruitment as evidenced by an increased number of MUs with relatively large spike amplitude. Our data suggest that the surface EMG amplitude could better represent the underlying MU activity during muscle fatigue and the frequency powers spectral shift may or may not reflect changes in MU recruitment and rate-coding patterns.     

Assessment of low back muscle fatigue by surface EMG signal analysis: methodological aspects.

This paper focuses on methodological issues related to surface electromyographic (EMG) signal detection from the low back muscles. In particular, we analysed (1) the characteristics (in terms of propagating components) of the signals detected from these muscles; (2) the effect of electrode location on the variables extracted from surface EMG; (3) the effect of the inter-electrode distance (IED) on the same variables; (4) the possibility of assessing fatigue during high and very low force level contractions. To address these issues, we detected single differential surface EMG signals by arrays of eight electrodes from six locations on the two sides of the spine, at the levels of the first (L1), the second (L2), and the fifth (L5) lumbar vertebra. In total, 42 surface EMG channels were acquired at the same time during both high and low force, short and long duration contractions. The main results were: (1) signal quality is poor with predominance of non-travelling components; (2) as a consequence of point (1), in the majority of the cases it is not possible to reliably estimate muscle fiber conduction velocity; (3) despite the poor signal quality, it was possible to distinguish the fatigue properties of the investigated muscles and the fatigability at different contraction levels; (4) IED affects the sensitivity of surface EMG variables to electrode location and large IEDs are suggested when spectral and amplitude analysis is performed; (5) the sensitivity of surface EMG variables to changes in electrode location is on average larger than for other muscles with less complex architecture; (6) IED influences amplitude initial values and slopes, and spectral variable initial values; (7) normalized slopes for both amplitude and spectral variables are not affected by IED and, thus, are suggested for fatigue analysis at different postures or during movement, when IED may change in different conditions (in case of separated electrodes); (8) the surface EMG technique at the global level of amplitude and spectral analysis cannot be used to characterize fatigue properties of low back muscles during very low level, long duration contractions since in these cases the non-stable MU pool has a major influence on the EMG variables. These considerations clarify issues only partially investigated in past studies. The limitations indicated above are important and should be carefully discussed when presenting surface EMG results as a means for low back muscle assessment in clinical practice.     

The effects of interelectrode distance over the innervation zone and normalization on the electromyographic amplitude and mean power frequency versus concentric,eccentric, and isometric torque relationships for the vastus lateralis muscle

The purpose of this study was to examine the influence of interelectrode distance (IED) over the estimated innervation zone (IZ) for the vastus lateralis muscle and normalization on the torque-related patterns of responses for electromyographic (EMG) amplitude and mean power frequency (MPF) during concentric isokinetic, eccentric isokinetic, and isometric muscle actions of the leg extensors. Eight men performed submaximal to maximal concentric isokinetic, eccentric isokinetic, and isometric muscle actions of the dominant leg extensors. Surface EMG signals were recorded simultaneously with two bipolar electrode arrangements in single differential configuration (20 and 40 mm IEDs) placed over the estimated IZ for the vastus lateralis muscle and a third electrode arrangement in single differential configuration (20 mm IED) placed distal to the estimated IZ. The results indicated that there were only a few (six of 90 statistical comparisons) significant (p < 0.05) mean differences among the three electrode arrangements for absolute EMG amplitude. There were no mean differences among the three electrode arrangements for absolute or normalized EMG MPF values or normalized EMG amplitude for the three types of muscle actions. Thus, it may be possible to reduce the potential influence of the IZ on amplitude and spectral parameters of the EMG signal through normalization.     

Motor unit activity and surface electromyogram power spectrum during increasing force of contraction

Twelve male subjects were tested to determine the relationship between motor unit (MU) activities and surface electromyogram (EMG) power spectral parameters with contractions increasing linearly from zero to 80% of maximal voluntary contraction (MVC). Intramuscular spike and surface EMG signals recorded simultaneously from biceps brachii were analyzed by means of a computer-aided intramuscular MU spike amplitude-frequency (ISAF) histogram and an EMG frequency power spectral analysis. All measurements were made in triplicate and averaged. Results indicate that there were highly significant increases in surface EMG amplitude (71 +/- 31.3 to 505 +/- 188 microV, p less than 0.01) and mean power frequency (89 +/- 13.3 to 123 +/- 23.5 Hz, p less than 0.01) with increasing force. These changes were accompanied by progressive increases in the firing frequency of MU's initially recruited, and of newly recruited MU's with relatively larger spike amplitudes. The group data in the ISAF histograms revealed significant increases in mean spike amplitude (412 +/- 79 to 972 +/- 117 microV, p less than 0.01) and mean firing frequency (17.8 +/- 5.4 to 24.7 +/- 4.1 Hz, p less than 0.01). These data suggest that surface EMG spectral analysis can provide a sensitive measure of the relative changes in MU activity during increasing force output.     

基于独立元分析和联合小波熵检测多导联ECG信号的QRS

QRS波群的准确定位是ECG信号自动分析的基础。为提高QRS检测率,提出一种基于独立元分析（ICA）和联合小波熵（CWS）检测多导联ECG信号QRS的算法。ICA算法从滤波后的多导联ECG信号中分离出对应心室活动的独立元;然后对各独立元进行连续小波变换（CWT）,重构小波系数的相空间,结合相空间中的QRS信息对独立元排序;最后检测排序后独立元的CWS得到QRS信息。实验对St.Petersburg12导联心率失常数据库及64导联犬心外膜数据库测试,比较本文算法与单导联QRS检测算法和双导联QRS检测算法的性能。结果表明,该文算法的性能最好,检测准确率分别为99.98%和100%。     

The effects of electrode placement and innervation zone location on the electromyographic amplitude and mean power frequency versus isometric torque relationships for the vastus lateralis muscle.

The purposes of this investigation were to examine the effects of electrode placement and innervation zone (IZ) location on: (a) the torque-related patterns of responses for absolute and normalized electromyographic (EMG) amplitude and mean power frequency (MPF) and (b) the mean absolute and normalized EMG amplitude and MPF values. In addition, the present study examined the variability between subjects for the location of the IZ for the vastus lateralis (VL). Eight men (mean+/-SD age=23.0+/-4.3yr) performed submaximal to maximal isometric muscle actions of the dominant leg extensors. During each muscle action, fifteen channels of bipolar surface EMG signals were detected from the vastus lateralis using a linear electrode array aligned with the long axis of the muscle fibers. The results indicated that there were differences among channels 1-15 for the patterns of responses and mean values for absolute and normalized EMG amplitude and MPF versus isometric torque. Thus, normalized EMG amplitude and MPF values from different individuals cannot be compared if the EMG signals were detected from different locations over the muscle. In addition, absolute and relative (to femur length) estimates of IZ location for the VL resulted in similar inter-subject variability.     

Modeling nonlinear errors in surface electromyography due to baseline noise: A new methodology

The surface electromyographic (EMG) signal is often contaminated by some degree of baseline noise. It is customary for scientists to subtract baseline noise from the measured EMG signal prior to further analyses based on the assumption that baseline noise adds linearly to the observed EMG signal. The stochastic nature of both the baseline and EMG signal, however, may invalidate this assumption. Alternately, “true” EMG signals may be either minimally or nonlinearly affected by baseline noise. This information is particularly relevant at low contraction intensities when signal-to-noise ratios (SNR) may be lowest. Thus, the purpose of this simulation study was to investigate the influence of varying levels of baseline noise (approximately 2–40% maximum EMG amplitude) on mean EMG burst amplitude and to assess the best means to account for signal noise. The simulations indicated baseline noise had minimal effects on mean EMG activity for maximum contractions, but increased nonlinearly with increasing noise levels and decreasing signal amplitudes. Thus, the simple baseline noise subtraction resulted in substantial error when estimating mean activity during low intensity EMG bursts. Conversely, correcting EMG signal as a nonlinear function of both baseline and measured signal amplitude provided highly accurate estimates of EMG amplitude. This novel nonlinear error modeling approach has potential implications for EMG signal processing, particularly when assessing co-activation of antagonist muscles or small amplitude contractions where the SNR can be low.     

Epoch length to accurately estimate the amplitude of interference EMG is likely the result of unavoidable amplitude cancellation

Researchers and clinicians routinely rely on interference electromyograms (EMGs) to estimate muscle forces and command signals in the neuromuscular system (e.g., amplitude, timing, and frequency content). The amplitude cancellation intrinsic to interference EMG, however, raises important questions about how to optimize these estimates. For example, what should the length of the epoch (time window) be to average an EMG signal to reliably estimate muscle forces and command signals? Shorter epochs are most practical, and significant reductions in epoch have been reported with high-pass filtering and whitening. Given that this processing attenuates power at frequencies of interest (<250 Hz), however, it is unclear how it improves the extraction of physiologically relevant information. We examined the influence of amplitude cancellation and high-pass filtering on the epoch necessary to accurately estimate the “true” average EMG amplitude calculated from a 28 s EMG trace (EMG_ref) during simulated constant isometric conditions. Monte Carlo iterations of a motor-unit model simulating 28 s of surface EMG produced 245 simulations under two conditions: with and without amplitude cancellation. For each simulation, we calculated the epoch necessary to generate average full-wave rectified EMG amplitudes that settled within 5% of EMGref. For the no-cancellation EMG, the necessary epochs were short (e.g., <100 ms). For the more realistic interference EMG (i.e., cancellation condition), epochs shortened dramatically after using high-pass filter cutoffs above 250 Hz, producing epochs short enough to be practical (i.e., <500 ms). We conclude that the need to use long epochs to accurately estimate EMG amplitude is likely the result of unavoidable amplitude cancellation, which helps to clarify why high-pass filtering (>250 Hz) improves EMG estimates.     

Frequency characteristics of signals and instrumentation: implication for EMG biofeedback studies

Signals can be analyzed in either the time or frequency domain. In the time domain, the analysis consists of manipulating and measuring one or more characteristics of the signal that may vary with time. One can, for instance, rectify a signal, filter it, calculate its mean value, display the histogram of its amplitude, and so forth. Frequency analysis is less well understood because it requires a lengthy mathematical treatment most easily done by computer. However, it gives exclusive information on a signal. For instance, when the frequency content of a signal is known, it is easy to specify which characteristics an amplifier must have in order to amplify the signal without distortion, or to set the cutoff frequencies of filters to eliminate noise. Also, in many circumstances, frequency spectra are more easily interpreted than the original raw data. Such is the case with the EMG where the random aspect of the signal makes some form of processing (i.e., rectification, filtering, etc.) necessary, but not always as meaningful as we would like. Thus we present here the principal characteristics of frequency analysis, and discuss its usefulness in analyzing EMG signals and its application to biofeedback, clinical practice, and research.     

Gender and muscle differences in EMG amplitude and median frequency, and variability during maximal voluntary contractions of the quadriceps femoris.

The purpose of this study was to evaluate gender and muscle differences in electromyographic (EMG) amplitude and median frequency mean and standard deviation during maximal voluntary contractions of the quadriceps femoris. Thirty recreationally active volunteers were assessed for isometric EMG activity of the vastus medialis (VM), vastus lateralis (VL), and rectus femoris (RF) muscles during three 5-s maximal isometric voluntary contractions (MVCs). Median frequency of the three muscles was assessed through a power spectral analysis (fast Fourier transformation, Hanning window processing, 512 points). The power spectral analysis was performed during the middle 3 s of each contraction over 11 consecutive, 512 ms epochs overlapping each other by half their length (256 ms). The median frequency (F(med)) for each of the 11 windows was determined for each muscle. The mean and standard deviation of the F(med) across the 11 overlapping windows were then calculated for each contraction and muscle. EMG amplitude was determined by calculating the root mean square (RMS-50 ms time constant) over the same contraction period for each muscle. The mean amplitude and standard deviation about the mean value were then determined. A three-factor ANOVA with repeated measures was performed on the calculated F(med) mean and standard deviation values, and RMS standard deviations, to assess any gender, muscle, or trial differences, or interactions. A two-factor (gender by muscle) ANOVA with repeated measures was performed on the RMS mean amplitude for each muscle. Intraclass correlation coefficients (ICCs-2,1), standard errors of measurement (SEMs), and associated 95% confidence intervals were then calculated for maximal quadriceps torque and F(med) for each muscle. The results from this study demonstrated that the VL muscle displayed significantly higher F(med) values than the RF and VM muscles. The RF muscle showed significantly higher F(med) values (mean of 11 overlapping windows) than the VM muscle. Intrasession reliability was found to be high for the calculated mean values (ICC=0.85-0.96), but was shown to be low for variability (ICC=0.13-0.45). The major findings of this study support the notion that the EMG signal is "quasi-random" in nature, as demonstrated by the reproducible F(med) means and unreliable variability.     

The effects of interelectrode distance on electromyographic amplitude and mean power frequency during isokinetic and isometric muscle actions of the biceps brachii.

The purpose of this study was to examine the effects of interelectrode distance (IED) on the absolute and normalized electromyographic (EMG) amplitude and mean power frequency (MPF) versus isokinetic and isometric torque relationships for the biceps brachii muscle. Ten adults [mean+/-SD age=22.0+/-3.4 years] performed submaximal to maximal, isokinetic and isometric muscle actions of the dominant forearm flexors. Following determination of isokinetic peak torque (PT) and the isometric maximum voluntary contraction (MVC), the subjects performed randomly ordered, submaximal step muscle actions in 10% increments from 10% to 90% PT and MVC. Surface EMG signals were recorded simultaneously from bipolar electrode arrangements placed over the biceps brachii muscle with IEDs of 20, 40, and 60mm. Absolute and normalized EMG amplitude (muVrms and %max) increased linearly with torque during the isokinetic and isometric muscle actions (r(2) range=0.988-0.998), but there were no significant changes for absolute or normalized EMG MPF (Hz or %max) from 10% to 100% PT and MVC. In some cases, there were significant (p<0.05) differences among the three IED arrangements for absolute EMG amplitude and MPF values, but not for the normalized values. These findings suggested that for the biceps brachii muscle, IEDs between 20 and 60mm resulted in similar patterns for the EMG amplitude or MPF versus dynamic and isometric torque relationships. Furthermore, unlike the absolute EMG amplitude and MPF values, the normalized EMG data were not influenced by changes in IED between 20 and 60mm. Thus, normalized EMG data can be compared among previous studies that have utilized different IED arrangements.