Estimating and minimizing movement artifacts in surface electromyogram

While recording surface electromyography [sEMG], it is possible to record the electrical activities coming from the muscles and transients in the half-cell potential at the electrode–electrolyte interface due to micromovements of the electrode–skin interface. Separating the two sources of electrical activity usually fails due to the overlapping frequency characteristics of the signals. This paper aims to develop a method that detects movement artifacts and suggests a minimization technique. Towards that aim, we first estimated the frequency characteristics of movement artifacts under various static and dynamic experimental conditions. We found that the extent of the movement artifact depended on the nature of the movement and varied from person to person. Our study's highest movement artifact frequency for the stand position was 10 Hz, tiptoe 22, walk 32, run 23, jump from box 41, and jump up and down 40 Hz. Secondly, using a 40 Hz highpass filter, we cut out most of the frequencies belonging to the movement artifacts. Finally, we checked whether the latencies and amplitudes of reflex and direct muscle responses were still observed in the highpass-filtered sEMG. We showed that the 40 Hz highpass filter did not significantly alter reflex and direct muscle variables. Therefore, we recommend that researchers who use sEMG under similar conditions employ the recommended level of highpass filtering to reduce movement artifacts from their records. However, suppose different movement conditions are used. In that case, it is best to estimate the frequency characteristics of the movement artifact before applying any highpass filtering to minimize movement artifacts and their harmonics from sEMG.     

Detection of surface electromyography recording time interval without muscle fatigue effect for biceps brachii muscle during maximum voluntary contraction

The effects of fatigue on maximum voluntary contraction (MVC) parameters were examined by using force and surface electromyography (sEMG) signals of the biceps brachii muscles (BBM) of 12 subjects. The purpose of the study was to find the sEMG time interval of the MVC recordings which is not affected by the muscle fatigue. At least 10 s of force and sEMG signals of BBM were recorded simultaneously during MVC. The subjects reached the maximum force level within 2 s by slightly increasing the force, and then contracted the BBM maximally. The time index of each sEMG and force signal were labeled with respect to the time index of the maximum force (i.e. after the time normalization, each sEMG or force signal’s 0 s time index corresponds to maximum force point). Then, the first 8 s of sEMG and force signals were divided into 0.5 s intervals. Mean force, median frequency (MF) and integrated EMG (iEMG) values were calculated for each interval. Amplitude normalization was performed by dividing the force signals to their mean values of 0 s time intervals (i.e. ?0.25 to 0.25 s). A similar amplitude normalization procedure was repeated for the iEMG and MF signals. Statistical analysis (Friedman test with Dunn’s post hoc test) was performed on the time and amplitude normalized signals (MF, iEMG). Although the ANOVA results did not give statistically significant information about the onset of the muscle fatigue, linear regression (mean force vs. time) showed a decreasing slope (Pearson-r = 0.9462, p < 0.0001) starting from the 0 s time interval. Thus, it might be assumed that the muscle fatigue starts after the 0 s time interval as the muscles cannot attain their peak force levels. This implies that the most reliable interval for MVC calculation which is not affected by the muscle fatigue is from the onset of the EMG activity to the peak force time. Mean, SD, and range of this interval (excluding 2 s gradual increase time) for 12 subjects were 2353, 1258 ms and 536–4186 ms, respectively. Exceeding this interval introduces estimation errors in the maximum amplitude calculations of MVC–sEMG studies for BBM. It was shown that, simultaneous recording of force and sEMG signals was required to calculate the maximum amplitude of the MVC–sEMG more accurately.     

Rectification of SEMG as a tool to demonstrate synchronous motor unit activity during vibration

The use of surface electromyography (SEMG) in vibration studies is problematic since motion artifacts occupy the same frequency band with the SEMG signal containing information on synchronous motor unit activity. We hypothesize that using a harsher, 80–500 Hz band-pass filter and using rectification can help eliminate motion artifacts and provide a way to observe synchronous motor unit activity that is phase locked to vibration using SEMG recordings only. Multi Motor Unit (MMU) action potentials using intramuscular electrodes along with SEMG were recorded from the gastrocnemius medialis (GM) of six healthy male volunteers. Data were collected during whole body vibration, using vibration frequencies of 30 Hz, 35 Hz, 40 Hz or 50 Hz. A computer simulation was used to investigate the efficacy of filtering under different scenarios: with or without artifacts and/or motor unit synchronization. Our findings indicate that motor unit synchronization took place during WBV as verified by MMU recordings. A harsh filtering regimen along with rectification proved successful in demonstrating motor unit synchronization in SEMG recordings. Our findings were further supported by the results from the computer simulation, which indicated that filtering and rectification was efficient in discriminating motion artifacts from motor unit synchronization. We suggest that the proposed signal processing technique may provide a new methodology to evaluate the effects of vibration treatments using only SEMG. This is a major advantage, as this non-intrusive method is able to overcome movement artifacts and also indicate the synchronization of underlying motor units.     

A new parameter for quantifying the variability of surface electromyographic signals during gait: The occurrence frequency

Natural variability of myoelectric activity during walking was recently analyzed considering hundreds of strides. This allowed assessing a parameter seldom considered in classic surface EMG (sEMG) studies: the occurrence frequency, defined as the frequency each muscle activation occurs with, quantified by the number of strides when a muscle is recruited with that specific activation modality. Aim of present study was to propose the occurrence frequency as a new parameter for assessing sEMG-signal variability during walking. Aim was addressed by processing sEMG signals acquired from Gastrocnemius Lateralis, Tibialis Anterior, Rectus Femoris and Biceps femoris in 40 healthy subjects in order to: (1) show that occurrence frequency is not correlated with ON/OFF instants (R_mean = 0.11 ± 0.07; P > 0.05) and total time of activation (R_mean = 0.15 ± 0.08; P > 0.05); (2) confirm the above results by two handy examples of application (analysis of gender and age) which highlighted that significant (P < 0.05) gender-related and age-related differences within population were detected in occurrence frequency, but not in temporal sEMG parameters. In conclusion, present study demonstrated that occurrence frequency is able to provide further information, besides those supplied by classical temporal sEMG parameters and thus it is suitable to complement them in the evaluation of variability of myoelectric activity during walking.     

Characteristics of EMG frequency bands in temporomandibullar disorders patients

The aim of the present study was to determine whether any specific frequency bands of surface electromyographic (sEMG) signals are more susceptible to alterations in patients with temporomandibular disorders (TMD), when compared with healthy subjects. Twenty-seven healthy adults (19 women and eight men; mean age: 23 ± 6.68 years) and 27 TMD patients (20 women and seven men; mean age: 24 ± 5.89 years) voluntarily participated in the experiment. sEMG data were recorded from the right and left masseter muscles (RM and LM) and the right and left anterior temporalis muscles (RT and LT) as the participants performed tests of chewing (CHW) and maximal clenching effort (MCE). Frequency domain analysis of the sEMG signal was used to analyze differences between TMD patients and healthy subjects in relation to the Power Spectral Density Function (PSDF). The analysis focused on the median frequency (MDF) of the sEMG signal and PSDF frequency bands after the EMG spectrum was divided into twenty-five frequency band of 20 Hz each. The Mann-Whitney test was used to compare MDF between TMD patients and healthy subjects and the frequency bands were analyzed using three-way ANOVA with three factors: frequency band, muscle and group. The results of the analysis confirmed that the median frequency values in TMD patients were significantly higher (p < 0.05) than those recorded for healthy subjects in the two experimental conditions (MCE and CHW), for all of the muscles assessed (RM, LM, RT and LT). In addition, frequency content between 20 and 100 Hz of the normalized PSDF range was significantly lower (p < 0.05) in TMD patients than in healthy. This study contributes to quantitatively identify TMD dysfunctions, by non-invasive sEMGs; this assessment is clinically important and still lacking nowadays.     

Biceps brachii myoelectric and oxygenation changes during static and sinusoidal isometric exercises

Surface myoelectric signal changes occurring during sustained isometric contractions have been extensively studied with quantitative surface electromyography (sEMG) and are described by means of some sEMG global variables in time and frequency domain (such as the median power spectral frequency). Recently, the possibility of studying local muscle O₂ saturation during exercise using non-invasive methods has been enhanced thanks to the use of near-infrared spectroscopy (NIRS). The purpose of this work was to combine NIRS and sEMG techniques to analyze the relationship between modifications of sEMG parameters and the underlying metabolic status of the exercising biceps brachii muscle. This relationship was tested under different isometric contraction modalities, namely static (ST) at 20, 40, 60 and 80%MVC and sinusoidal (SIN) at 40 ± 20 and 60 ± 20%MVC. Results clearly indicated the presence of an initial fast phase of muscle O₂ desaturation followed by a slow phase, regardless of the contraction modality. Moreover, the initial rate of muscle O₂ desaturation was related to the level of force output (R = 0.92), but it was independent on the contraction modality (p < 0.05). Similarly, changes in sEMG parameters were related to force level (Conduction Velocity-CV vs. Force: R = 0.87; sEMG Median Frequency-MDF vs. Force: R = 0.86). The high correlation found between CV-MDF and Tissue Oxygenation Index (TOI) slope (R = 0.73 and 0.72, respectively) suggests a strong relationship between NIRS and sEMG data. This study indicates that muscle O₂ demand during isometric contractions from low to high force levels is influenced by the type of active motor units and not from the type of isometric exercise modality.     

Assessment of the non-Gaussianity and non-linearity levels of simulated sEMG signals on stationary segments

The purpose of this paper was to evaluate the effects of the longitudinal single differential (LSD), the longitudinal double differential (LDD) and the normal double differential (NDD) spatial filters, the electrode shape, the inter-electrode distance (IED) on non-Gaussianity and non-linearity levels of simulated surface EMG (sEMG) signals when the maximum voluntary contraction (MVC) varied from 10% to 100% by a step of 10%. The effects of recruitment range thresholds (RR), the firing rate (FR) strategy and the peak firing rate (PFR) of motor units were also considered.A cylindrical multilayer model of the volume conductor and a model of motor unit (MU) recruitment and firing rate were used to simulate sEMG signals in a pool of 120 MUs for 5 s. Firstly, the stationarity of sEMG signals was tested by the runs, the reverse arrangements (RA) and the modified reverse arrangements (MRA) tests. Then the non-Gaussianity was characterised with bicoherence and kurtosis, and non-linearity levels was evaluated with linearity test.The kurtosis analysis showed that the sEMG signals detected by the LSD filter were the most Gaussian and those detected by the NDD filter were the least Gaussian. In addition, the sEMG signals detected by the LSD filter were the most linear. For a given filter, the sEMG signals detected by using rectangular electrodes were more Gaussian and more linear than that detected with circular electrodes. Moreover, the sEMG signals are less non-Gaussian and more linear with reverse onion-skin firing rate strategy than those with onion-skin strategy. The levels of sEMG signal Gaussianity and linearity increased with the increase of the IED, RR and PFR.     

Speed-dependent variation in the Piper rhythm

Rhythmic fluctuations (Piper rhythm) have been resolved in surface electromyography (sEMG) recordings. For a long time this periodic signal could only be detected in isometric contractions, where the innervation time is much longer than the oscillation period. However, recently a periodic signal could also be resolved in cyclic movements like running, using non-linear scaled wavelets.The purpose of this study was to characterise the rhythm for different running velocities. A rhythmic signal (the Piper rhythm) was found in the EMG signal of both bellies of the gastrocnemius muscle with a frequency range of 20–35 Hz. A decrease of the Piper frequency could be observed for all of the 13 subjects when increasing the running speed from 1.3 to 4.9 m/s. The time and frequency analysis indicated that the origin of the periodic signal is from the activation of the muscle and is not likely resulting from muscle vibrations. No correlation between the medial and the lateral part of the muscle bellies could be found indicating that the two muscle compartments are fine tuned by two distinct processes. According to the literature, the signal might be the manifestation of a cortical rhythm, which has been shown for isokinetic movements.     

Quadriceps EMG frequency content following isometric lumbar extension exercise

A relationship exists between muscles of the lumbar spine and those of the lower extremity where the quadriceps become more inhibited after lumbar paraspinal. The purpose of this experiment was to compare surface electromyography (sEMG) total frequency content after lumbar paraspinal fatiguing exercise. Scope: 50 subjects performed fatiguing lumbar extension exercise indexed by downward shifts in median frequency calculated from lumbar paraspinal sEMG signal. Before and after each exercise set we recorded maximal, isometric knee extension torque and quadriceps central activation ratio (QI) using the superimposed burst technique while recording vastus lateralis sEMG. We calculated total frequency content of the sEMG signal (fEMG_TOTAL) as the area of the quadriceps sEMG frequency spectrum. Quadriceps fEMG_TOTAL decreased from baseline following the first and second exercise sets. There was no significant change in quadriceps sEMG median frequency among baseline and post-exercise measures. The change in fEMG_TOTAL was correlated with the change in QI following the first (r = ?0.41, P = 0.003) and second (r = ?0.32, P = 0.02) exercise sets. Conclusion: Quadriceps fEMG_TOTAL decreased following fatiguing lumbar extension exercise, in the absence of a significant change in quadriceps median frequency.     

The effect of perspiration on the sEMG amplitude and power spectrum

Sweat accumulation underneath surface EMG (sEMG) electrodes is a common problem in workplace studies which compromises electrode adherence to the skin as well as signal fidelity. In this study, the effect of sweat accumulation on signal amplitude and mean frequency (MF) was examined to determine if the sEMG signal becomes altered through the sweat layer and whether this effect can be avoided by interrupting the pool of sweat using a thin strip of medical adhesive between the electrode snaps. Nine males performed a maximum, isometric contraction of their right quadriceps as sEMG was collected. Skin conditions under the electrode were dry and wet in incremental layers of 0.02 mm of artificial sweat. The results demonstrated that sweat accumulation under sEMG electrodes dampens the amplitude of the EMG signal in a predictable way (r = .88 and .97 for double and single snap electrodes, respectively) with almost 2% and 3% deterioration for every 0.02 mm of sweat depending on the type of electrode used. The medical adhesive proved to be highly effective at preventing amplitude deterioration indicating that signal shunting can be prevented. MF was not influenced by sweat accumulation even under the extreme wet condition.     

A novel approach for removing ECG interferences from surface EMG signals using a combined ANFIS and wavelet

In recent years, the removal of electrocardiogram (ECG) interferences from electromyogram (EMG) signals has been given large consideration. Where the quality of EMG signal is of interest, it is important to remove ECG interferences from EMG signals. In this paper, an efficient method based on a combination of adaptive neuro-fuzzy inference system (ANFIS) and wavelet transform is proposed to effectively eliminate ECG interferences from surface EMG signals. The proposed approach is compared with other common methods such as high-pass filter, artificial neural network, adaptive noise canceller, wavelet transform, subtraction method and ANFIS. It is found that the performance of the proposed ANFIS–wavelet method is superior to the other methods with the signal to noise ratio and relative error of 14.97 dB and 0.02 respectively and a significantly higher correlation coefficient (p < 0.05).     

Improving surface EMG burst detection in infrahyoid muscles during swallowing using digital filters and discrete wavelet analysis

The visual inspection is a widely used method for evaluating the surface electromyographic signal (sEMG) during deglutition, a process highly dependent of the examiners expertise. It is desirable to have a less subjective and automated technique to improve the onset detection in swallowing related muscles, which have a low signal-to-noise ratio. In this work, we acquired sEMG measured in infrahyoid muscles with high baseline noise of ten healthy adults during water swallowing tasks. Two methods were applied to find the combination of cutoff frequencies that achieve the most accurate onset detection: discrete wavelet decomposition based method and fixed steps variations of low and high cutoff frequencies of a digital bandpass filter. Teager-Kaiser Energy operator, root mean square and simple threshold method were applied for both techniques. Results show a narrowing of the effective bandwidth vs. the literature recommended parameters for sEMG acquisition. Both level 3 decomposition with mother wavelet db4 and bandpass filter with cutoff frequencies between 130 and 180 Hz were optimal for onset detection in infrahyoid muscles. The proposed methodologies recognized the onset time with predictive power above 0.95, that is similar to previous findings but in larger and more superficial muscles in limbs.     

Changes in muscle geometry during forearm pronation and supination and their relationships to EMG cross-correlation measures

Geometric artifact may alter the amplitude and frequency of the electromyography (EMG) signal. Artifacts include the changing geometry of muscles with respect to electrodes and potential crosstalk from adjacent muscles. This study addresses: (1) the geometrical relationships between common electrode placement sites for six forearm muscles, (2) the geometrical change of forearm muscles in pronation and supination, and (3) the relationships between EMG cross-correlation and muscle geometry. EMG and ultrasonography images were recorded during pronation, supination, and neutral forearm postures while exerting 20% maximum grip strength. Proportions of anatomical structures were then calculated for 15 mm, 20 mm, and 25 mm radial pick-up zone distances, representing greater than 90% of observed myoelectrical signal energy. We found that guidelines for electrode placements were supported and no single posture maximized the proportion of the target muscle detected. Secondly, other muscles were present in the most conservative 15 mm radius pick up zone; it is unlikely that surface EMG can completely differentiate between forearm muscle activities. Thirdly, forearm orientation did not appear to be an important factor in changing the geometrical relationships between surface electrodes and the muscles studied, and fourthly, certain muscles (e.g., FDS) may be more vulnerable to EMG crosstalk.     

Uncovering patterns of forearm muscle activity using multi-channel mechanomyography

A coordinated activation of distal forearm muscles allows the hand and fingers to be shaped during movement and grasp. However, little is known about how the muscle activation patterns are reflected in multi-channel mechanomyogram (MMG) signals. The purpose of this study is to determine if multi-site MMG signals exhibit distinctive patterns of forearm muscle activity. MMG signals were recorded from forearm muscle sites of nine able-bodied participants during hand movement. By using 14 features selected by a genetic algorithm and classified by a linear discriminant analysis classifier (LDA), we show that MMG patterns are specific and consistent enough to identify 7 ± 1 hand movements with an accuracy of 90 ± 4%. MMG-based movement recognition required a minimum of three recording sites. Further, by classifying five classes of contraction patterns with 98 ± 3% accuracy from MMG signals recorded from the residual limb of an amputee participant, we demonstrate that MMG shows pattern-specificity even in the absence of typical musculature. Multi-site monitoring of the RMS of MMG signals is suggested as a method of estimating the relative contributions of muscles to motor tasks. The patterns in MMG facilitate our understanding of the mechanical activity of muscles during movement.     

Reliability and minimal detectable change in scapulothoracic neuromuscular activity

Alterations in scapular muscle activity, including excess activation of the upper trapezius (UT) and onset latencies of the lower trapezius (LT) and serratus anterior (SA) muscles, are associated with abnormal scapular motion and shoulder impingement. Limited information exists on the reliability of neuromuscular activity to demonstrate the efficacy of interventions. The purpose of this study was to characterize the reproducibility of scapular muscle activity (mean activity, relative onset timing) over time and establish the minimal detectable change (MDC). Surface electromyography (sEMG) of the UT, LT, SA and anterior deltoid (AD) muscles in 16 adults were captured during an overhead lifting task in two sessions, one-week apart. sEMG data were also normalized to maximum isometric contraction and the relative onset and mean muscle activity during concentric and eccentric phases of the scapular muscles were calculated. Additionally, reliability of the absolute sEMG data during the lifting task and MVIC was evaluated. Both intrasession and intersession reliability of normalized and absolute mean scapular muscle activity, assessed with intraclass correlation coefficients (ICC), ranged from 0.62 to 0.99; MDC values were between 1.3% and 11.7% MVIC and 24 to 135 mV absolute sEMG. Reliability of sEMG during MVIC was ICC = 0.82–0.99, with the exception of intersession upper trapezius reliability (ICC = 0.36). Within session reliability of muscle onset times was ICC = 0.88–0.97, but between session reliability was lower with ICC = 0.43–0.73; MDC were between 39 and 237 ms. Small changes in scapular neuromuscular mean activity (>11.7% MVIC) can be interpreted as meaningful change, while change in muscle onset timing in light of specific processing parameters used in this study is more variable.     

Intramuscular fine-wire electromyography during cycling: Repeatability,normalisation and a comparison to surface electromyography

This study investigated (a) the feasibility and repeatability of intramuscular fine-wire electromyographic (fEMG) recordings from leg muscles during the repetitive, high-velocity cycling movement, (b) the influence of amplitude normalization technique on repeatability and statistical sensitivity, (c) the influence of test-retest interval duration on repeatability, and (d) differences between fEMG and surface EMG (sEMG) recordings of cycling. EMG activity of leg muscles was recorded using surface and fine-wire electrodes during one (n = 12, to investigate statistical sensitivity and compare sEMG and fEMG) or two sessions (T1 and T2, 5–20 days apart, n = 10, to investigate repeatability). fEMG recordings were feasible and there was high repeatability of fEMG recordings normalised to maximum measured EMG amplitude (MAX); mean coefficients of multiple correlation (CMC) ranged from .83 ± .13 to .88 ± .07. Data normalised to maximal (MVC) or submaximal contractions (sMVC) were less repeatable (p < .01). Statistical sensitivity was also greatest for data normalised to MAX (p < .01). Repeatability of fEMG increased with greater test-retest intervals (p < .01). The global pattern of muscle recruitment was consistent between sEMG and fEMG but sEMG recordings were characterized by additional myoelectric content. These findings support and guide the use of fEMG techniques to investigate leg muscle recruitment during cycling.     

Time/frequency events of surface mechanomyographic signals resolved by nonlinearly scaled wavelets

The purpose of this investigation is to introduce a wavelet analysis designed for analyzing short events reflecting bursts of muscle activity in non-stationary mechanomyographic (MMG) signals. A filter bank of eleven nonlinearly scaled wavelets that maintain the optimal combination of time and frequency resolution across the frequency range of MMG signals (5–100 Hz) was used for the analysis. A comparison with the short-time Fourier transform, Wigner-Ville transform and continuous wavelet transform using a test signal with known time–frequency characteristics showed that the MMG wavelet analysis resolved the intensity, timing, and frequencies of events in a more distinct way without overemphasizing high or low frequencies or generating interference terms. The analysis was used to process MMG signals from the vastus lateralis, rectus femoris, and vastus medialis muscles obtained during maximal concentric and eccentric isokinetic movements. Muscular events were observed that were precisely located in time and frequency in a muscle-specific way, thereby showing periods of synergistic contractions of the quadriceps muscles. The MMG wavelet spectra showed different spectral bands for concentric and eccentric isokinetic movements. In addition, the high and low frequency bands seemed to be activated independently during the isokinetic movement. What generates these bands is not yet known, however, the MMG wavelet analysis was able to resolve them, and is therefore applicable to non-stationary MMG signals.     

The vastus lateralis neuromuscular activity during all-out cycling exercise

ObjectiveThe objective of this work was to study modifications in motor control through surface electromyographic (sEMG) activity during a very short all-out cycling exercise.MethodsTwelve male cyclists (age 23 ± 4 years) participated in this study. After a warm-up period, each subject performed three all-out cycling exercises of 6 s separated by 2 min of complete rest. This protocol was repeated three times with a minimum of 2 days between each session. The braking torque imposed on cycling motion was 19 N m. The sEMG of the vastus lateralis was recorded during the first seven contractions of the sprint. Time–frequency analysis of sEMG was performed using continuous wavelet transform. The mean power frequency (MPF, qualitative modifications in the recruitment of motor units) and signal energy (a quantitative indicator of modifications in the motor units recruitment) were computed for the frequency range 10–500 Hz.ResultssEMG energy increased (P ? 0.05) between contraction number 1 and 2, decreased (P ? 0.05) between contraction number 2 and 3 then stabilized between contraction number 3 and 7 during the all-out test. MPF increased (P ? 0.05) during the all-out test. This increase was more marked during the first two contractions.ConclusionsThe decrease in energy and the increase in the sEMG MPF suggest a large spatial recruitment of motor units (MUs) at the beginning of the sprint followed by a preferential recruitment of faster MUs at the end of the sprint, respectively.     

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.