Identification of isometric contractions based on High Density EMG maps

Identification of motion intention and muscle activation strategy is necessary to control human–machine interfaces like prostheses or orthoses, as well as other rehabilitation devices, games and computer-based training programs. Pattern recognition from sEMG signals has been extensively investigated in the last decades, however, most of the studies did not take into account different strengths and EMG distributions associated to the intended task. The identification of such quantities could be beneficial for the training of the subject or the control of assistive devices. Recent studies have shown the need to improve pattern-recognition classification by reducing sensitivity to changes in the exerted strength, muscle-electrode shifts and bad contacts. Surface High Density EMG (HD-EMG) obtained from 2-dimensional arrays can provide much more information than electrode pairs for inferring not only motion intention but also the strategy adopted to distribute the load between muscles as well as changes in the spatial distribution of motor unit action potentials within a single muscle because of it.The objectives of this study were: (a) the automatic identification of four isometric motor tasks associated with the degrees of freedom of the forearm: flexion–extension and supination–pronation and (b) the differentiation among levels of voluntary contraction at low-medium efforts. For this purpose, monopolar HD-EMG maps were obtained from five muscles of the upper-limb in healthy subjects. An original classifier is proposed, based on: (1) Two steps linear discriminant analysis of the EMG information for each type of contraction, and (2) features extracted from HD-EMG maps and related to its intensity and distribution in the 2D space. The classifier was trained and tested with different effort levels. Spatial distribution-based features by themselves are not sufficient to classify the type of task or the effort level with an acceptable accuracy; however, when calculated with the “isolated masses” method proposed in this study and combined with intensity-base features, the performance of the classifier is improved. The classifier is capable of identifying the tasks even at 10% of Maximum Voluntary Contraction, in the range of effort level developed by patients with neuromuscular disorders, showing that intention end effort of motion can be estimated from HD-EMG maps and applied in rehabilitation.     

Separation of propagating and non propagating components in surface EMG

Surface electromyogram (EMG) detected by electrode arrays along the muscle fibre direction can be approximated by the sum of propagating and non propagating components. A technique to separate propagating and non propagating components in surface EMG signals is developed. The first step is an adaptive filter, which allows obtaining an estimation of the delay between signals detected at different channels and a first estimate of propagating and non propagating components; the second step is used to optimise the estimation of the two components. The method is applicable to signals with one propagating and one non propagating component. It was optimised on simulated signals, and then applied to single motor unit action potentials (MUAP) and to electrically elicited EMG (M-waves).

The new method was first tested on phenomenological signals constituted by the sum of a propagating and a non propagating signal and then applied to simulated and experimental EMG signals. Simulated signals were generated by a cylindrical, layered volume conductor model. Experimental signals were monopolar surface EMG signals collected from the abductor pollicis brevis muscle and M-waves recorded during transcutaneous electrical stimulation of the biceps muscle. The technique may find different applications: in single motor unit (MU) studies (a) for decreasing the variability and bias of CV estimates due to the presence of the non propagating components, (b) for estimating automatically the length of the muscle fibres from only three detected channels and (c) for removal of the stimulation artifact M-waves.     

Using two-dimensional spatial information in decomposition of surface EMG signals.

Recently, high-density surface EMG electrode grids and multi-channel amplifiers became available for non-invasive recording of human motor units (MUs). We present a way to decompose surface EMG signals into MU firing patterns, whereby we concentrate on the importance of two-dimensional spatial differences between the MU action potentials (MUAPs). Our method is exemplified with high-density EMG data from the vastus lateralis muscle of a single subject. Bipolar and Laplacian spatial filtering was applied to the monopolar raw signals. From the single recording in this subject six different simultaneously active MUs could be distinguished using the spatial differences between MUAPs in the direction perpendicular to the muscle fiber direction. After spike-triggered averaging, 125-channel two-dimensional MUAP templates were obtained. Template-matching allowed tracking of all MU firings. The impact of spatial information was measured by using subsets of the MUAP templates, either in parallel or perpendicular to the muscle fiber direction. The use of one-dimensional spatial information perpendicular to the muscle fiber direction was superior to the use of a linear array electrode in the longitudinal direction. However, to detect the firing events of the MUs with a high accuracy, as needed for instance for estimation of firing synchrony, two-dimensional information from the complete grid electrode appears essential.     

Comparison of force and EMG measures in normal and reinnervated tibialis anterior muscles of the rat.

The relationship between motor unit force and the recorded voltage produced by activated muscle unit fibres (electromyogram, EMG) was examined in normal and reinnervated rat tibialis anterior muscles. The number, cross-sectional area, and radial distance from the recording electrode of muscle fibres in a given unit, obtained directly from a sample of glycogen-depleted motor units, were analysed in relation to the magnitude of the EMG signal produced by that unit. EMG peak to peak amplitude and area varied as approximately the square root of twitch force in both normal and reinnervated units. Furthermore, the EMG amplitude increased approximately as the total cross-sectional area of the motor unit (number of muscle fibres x the average cross-sectional area of the fibres) and inversely with approximately the square root of the distance of fibres from the recording electrodes on the surface of the muscle.     

Recent progress in the diagnostic use of surface EMG for neurological diseases.

The different techniques to measure and analyze surface EMG are summarized with an emphasis on the clinician's point of view. The application of surface EMG in neurological disease is hampered by many inherent problems, especially the difficulties in extracting features of single motor units. However, the evolution of surface EMG from single bipolar recordings via a linear array of multiple electrodes to densely packed, multi-channel electrode arrays could in principle solve this problem. The added value of using multiple channels (up to 128) with an interelectrode distance of a few millimetres to obtain more spatial information is emphasized. At least for some muscles it is now possible to extract information from the surface EMG, conventionally thought to belong to the domain of needle EMG (for example the "electrical size" of motor units). The use of analysis techniques such as the estimation of muscle fiber conduction velocity has already proven to be of diagnostic value in several myopathies characterized by a disturbed membrane function and in metabolic myopathies with abnormal fatigue profiles. Future research should be directed at the development of analysis techniques enabling the extraction of more relevant motor unit variables from surface EMG signals.     

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 effect of electrode placement on frontalis EMG measurement in headache patients

Comparisons were made between frontalis EMG surface recordings concurrently taken from horizontal, right vertical, and left vertical electrode placement configurations. Six migraine and seven tension headache patients were used as subjects. Results showed that, in general, a positive correlation existed between recordings from the three placements for both migraine and tension subjects as a group. However, marked individual differences were noted. It was concluded that electrode placement can be quite important in measuring changes in frontalis muscle tension for headache patients.     

Influence of the feature space on the estimation of hand grasping force from intramuscular EMG

The study compares the performance of different combinations of nine features extracted from intramuscular electromyogram (EMG) recordings for the estimation of grasping force within the range 0–100% maximum voluntary contraction (MVC). Single-channel intramuscular EMG was recorded from the flexor digitorum profundus (FDP) muscle from 11 subjects who exerted three force profiles during power grasping. The ability of the features to estimate force with a 1st order polynomial (poly1) and an artificial neural network (ANN) model was assessed using the adjusted coefficient of determination (R²). Willison amplitude (WAMP) and root mean square (RMS) showed the highest R² (～0.88) values for poly1. The performance of all the features to predict force significantly increased (P < 0.01) when an ANN was applied. In this case, the Modified Mean Absolute Value (MMAV) demonstrated the best performance (～0.91). The results showed that a single channel intramuscular EMG recording represents the entire grasping force range (0–100% MVC) measured from the FDP muscle. The association between EMG and force depends on the features extracted and on the model.     

Branched EMG electrodes for stable and selective recording of single motor unit potentials in humans.

Branched surface EMG electrodes are bipolar electrodes with the hot signal pole referenced to two or more short-circuited leading-off surfaces. This technique provides stable recording of single motor unit potentials during real movements, up to maximal muscle contractions. The selective characteristic of branched electrodes is based on the same principles as the double differential detection system and spatial filtering technique proposed later. Equi-weight calculations to assess the selectivity of different electrode types and their position are used. The main advantage of branched electrodes, especially high stability, is achieved by the wire electrode version. The design, manufacture, implementation, and application of wire electrodes are discussed in detail. During recording of motor unit potentials, electrodes are positioned subcutaneously over the muscle fascia. This positioning maximizes electrode stability. Appropriate orientation of the electrode relative to the muscle architecture ensures adequate selectivity for single motor unit recordings. Branched electrodes require ordinary EMG equipment (two or even one amplifier).     

EMG monitoring in functional electrostimulation]

When using functional electrical stimulation (FES), correct adjustment of stimulation parameters, and monitoring of the stimulated muscle is mandatory if tissue damage is to be avoided. Although several FES systems are already in regular use, a method for direct muscle monitoring is still lacking. This paper investigates the suitability of the electromyogram (EMG) for such a purpose. In six sheep, the right latissimus dorsi muscle (LDM) and the associated thoracodorsal nerve were exposed. Stimulation was effected via electrodes placed on the nerve. Three electrodes were placed in the LDM for EMG recording, and the tendon was connected to a force transducer for isometric force measurement. Stimulation was applied for one second (burst), followed by a three-second pause. The stimulation current was increased in 0.2 mA steps, starting at 0 mA and ending at 4 mA. Throughout the investigation, the EMG signal was monitored with an oscilloscope. In addition, the EMG signal and the force transducer signal were recorded for subsequent analysis. An analysis of the data of all six sheep revealed an almost linear relationship between muscle force and m-wave amplitude (magnitude of r = 0.95, p < 0.001). M-wave monitoring during EMG recording with three intramuscular electrodes is a reliable method of monitoring FES-induced muscle activity, but the absolute force cannot be measured.     

Inter-electrode spacing of surface EMG sensors: reduction of crosstalk contamination during voluntary contractions

We investigated the influence of inter-electrode spacing on the degree of crosstalk contamination in surface electromyographic (sEMG) signals in the tibialis anterior (target muscle), generated by the triceps surae (crosstalk muscle), using bar and disk electrode arrays. The degree of crosstalk contamination was assessed for voluntary constant-force isometric contractions and for dynamic contractions during walking. Single-differential signals were acquired with inter-electrode spacing ranging from 5 mm to 40 mm. Additionally, double differential signals were acquired at 10 mm spacing using the bar electrode array. Crosstalk contamination at the target muscle was expressed as the ratio of the detected crosstalk signal to that of the target muscle signal. The crosstalk contamination ratio approached a mean of 50% for the 40 mm spacing for triceps surae muscle contractions at 80% MVC and tibialis anterior muscle contractions at 10% MVC. For single differential recordings, the minimum crosstalk contamination was obtained from the 10 mm spacing. The results showed no significant differences between the bar and disk electrode arrays. During walking, the crosstalk contamination on the tibialis anterior muscle reached levels of 23% for a commonly used 22 mm spacing single-differential disk sensor, 17% for a 10 mm spacing single-differential bar sensor, and 8% for a 10 mm double-differential bar sensor. For both studies the effect of electrode spacing on crosstalk contamination was statistically significant. Crosstalk contamination and inter-electrode spacing should therefore be a serious concern in gait studies when the sEMG signal is collected with single differential sensors. The contamination can distort the target muscle signal and mislead the interpretation of its activation timing and force magnitude.     

Surface EMG signal processing during isometric contractions

This paper provides an overview of techniques suitable for the estimation, interpretation and understanding of time variations that affect the surface electromyographic (EMG) signal during sustained voluntary or electrically elicited contractions. These variations concern amplitude variables, spectral variables and muscle fiber conduction velocity, are interdependent and are referred to as the ‘fatigue plot'. The fatigue plot provides information suitable for the classification of muscle behavior. In addition, the information obtainable by means of linear electrode arrays is discussed, and applications of mathematical models for the interpretation of array signals are presented. The model approach provides additional ways for the classification of muscle behavior.     

Estimation of muscle fiber conduction velocity from two-dimensional surface EMG recordings in dynamic tasks

The aims of this study are (1) to demonstrate that multi-channel surface electromyographic (EMG) signals can be detected with negligible artifacts during fast dynamic movements with an adhesive two-dimensional (2D) grid of 64 electrodes and (2) to propose a new method for the estimation of muscle fiber conduction velocity from short epochs of 2D EMG recordings during dynamic tasks. Surface EMG signals were collected from the biceps brachii muscle of four subjects with a grid of 13 × 5 electrodes during horizontal elbow flexion/extension movements (range 120–170°) at the maximum speed, repeated cyclically for 2 min. Action potentials propagating between the innervation zone and tendon regions could be detected during the dynamic task. A maximum likelihood method for conduction velocity estimation from the 2D grid using short time intervals was developed and applied to the experimental signals. The accuracy of conduction velocity estimation, assessed from the standard deviation of the residual of the regression line with respect to time, decreased from (range) 0.20–0.33 m/s using one column to 0.02–0.15 m/s when combining five columns of the electrode grid. This novel method for estimation of muscle fiber conduction velocity from 2D EMG recordings provides an estimate which is global in space and local in time, thus representative of the entire muscle yet able to track fast changes over the execution of a task, as is required for assessing muscle properties during fast movements.     

Effect of muscle length on electromyogram in a canine diaphragm strip preparation

The relationship between diaphragm electromyogram (EMG), isometric force, and length was studied in the canine diaphragm strip with intact blood supply and innervation under three conditions: supramaximal tetanic (100 Hz) phrenic nerve stimulation (STPS; n = 12), supramaximal phrenic stimulation at 25 Hz (n = 15), and submaximal phrenic stimulation at 25 Hz (n = 5). In the same preparation, the EMG-length relationship was also examined with direct muscle stimulation when the neuromuscular junction was blocked. EMG from three different sites and via two types of electrodes (direct or sewn-in and surface) were recorded during isometric contraction at different lengths. Direct EMGs were recorded from two bipolar electrodes sutured into the strip, one near its central end and the other near its costal end. A third EMG electrode configuration summed potentials from the whole strip by recording potentials between central and costal sites. Surface EMGs were recorded by a bipolar spring clip electrode that made contact with upper and lower surfaces of the muscle strip with light pressure. In all conditions of stimulation with different types of electrodes, all EMGs decreased significantly (P less than 0.05) when muscle length was changed from 50 to 120% of resting length (L0). Minimal and maximal force outputs were observed at 50 and 120% of L0, respectively, in all experiments. The results of this study indicated that the muscle length is a significant variable that affects the EMG recording and that the diaphragmatic EMG may not be an accurate reflection of phrenic nerve activity.(ABSTRACT TRUNCATED AT 250 WORDS)     

Interpreting muscle function from EMG: lessons learned from direct measurements of muscle force

Electromyography is often used to infer the pattern of productionof force by skeletal muscles. The interpretation of muscle functionfrom the electromyogram (EMG) is challenged by the fact thatfactors such as type of muscle fiber, muscle length, and musclevelocity can all influence the relationship between electricaland mechanical activity of a muscle. Simultaneous measurementsof EMG, muscle force, and fascicle length in hindlimb musclesof wild turkeys allow us to probe the quantitative link betweenforce and EMG. We examined two features of the force–EMGrelationship. First, we measured the relaxation electromechanicaldelay (r-EMD) as the time from the end of the EMG signal totime of the end of force. This delay varied with locomotor speedin the lateral gastrocnemius (LG); it was longer at slow walkingspeeds than for running. This variation in r-EMD was not explainedby differences in muscle length trajectory, as the magnitudeof r-EMD was not correlated with the velocity of shorteningof the muscle during relaxation. We speculate that the longerrelaxation times at slow walking speeds compared with runningmay reflect the longer time course of relaxation in slower musclesfibers. We also examined the relationship between magnitudeof force and EMG across a range of walking and running speeds.We analyzed the force–EMG relationship during the swingphase separately from the force–EMG relationship duringstance phase. During stance, force amplitude (average force)was linearly related to mean EMG amplitude (average EMG). Forcesduring swing phase were lower than predicted from the stancephase force–EMG relationship. The different force–EMGrelationships during the stance and swing phases may reflectthe contribution of passive structures to the development offorce, or a nonlinear force–EMG relationship at low levelsof muscle activity. Together the results suggest that any inferenceof force from EMG must be done cautiously when a broad rangeof activities is considered.     

Methodological aspects of SEMG recordings for force estimation – A tutorial and review

Insight into the magnitude of muscle forces is important in biomechanics research, for example because muscle forces are the main determinants of joint loading. Unfortunately muscle forces cannot be calculated directly and can only be measured using invasive procedures. Therefore, estimates of muscle force based on surface EMG measurements are frequently used. This review discusses the problems associated with surface EMG in muscle force estimation and the solutions that novel methodological developments provide to this problem. First, some basic aspects of muscle activity and EMG are reviewed and related to EMG amplitude estimation. The main methodological issues in EMG amplitude estimation are precision and representativeness. Lack of precision arises directly from the stochastic nature of the EMG signal as the summation of a series of randomly occurring polyphasic motor unit potentials and the resulting random constructive and destructive (phase cancellation) superimpositions. Representativeness is an issue due the structural and functional heterogeneity of muscles. Novel methods, i.e. multi-channel monopolar EMG and high-pass filtering or whitening of conventional bipolar EMG allow substantially less variable estimates of the EMG amplitude and yield better estimates of muscle force by (1) reducing effects of phase cancellation, and (2) adequate representation of the heterogeneous activity of motor units within a muscle. With such methods, highly accurate predictions of force, even of the minute force fluctuations that occur during an isometric and isotonic contraction have been achieved. For dynamic contractions, EMG-based force estimates are confounded by the effects of muscle length and contraction velocity on force producing capacity. These contractions require EMG amplitude estimates to be combined with modeling of muscle contraction dynamics to achieve valid force predictions.     

Force Control Is Related to Low-Frequency Oscillations in Force and Surface EMG

Force variability during constant force tasks is directly related to oscillations below 0.5 Hz in force. However, it is unknown whether such oscillations exist in muscle activity. The purpose of this paper, therefore, was to determine whether oscillations below 0.5 Hz in force are evident in the activation of muscle. Fourteen young adults (21.07±2.76 years, 7 women) performed constant isometric force tasks at 5% and 30% MVC by abducting the left index finger. We recorded the force output from the index finger and surface EMG from the first dorsal interosseous (FDI) muscle and quantified the following outcomes: 1) variability of force using the SD of force; 2) power spectrum of force below 2 Hz; 3) EMG bursts; 4) power spectrum of EMG bursts below 2 Hz; and 5) power spectrum of the interference EMG from 10–300 Hz. The SD of force increased significantly from 5 to 30% MVC and this increase was significantly related to the increase in force oscillations below 0.5 Hz (R ² = 0.82). For both force levels, the power spectrum for force and EMG burst was similar and contained most of the power from 0–0.5 Hz. Force and EMG burst oscillations below 0.5 Hz were highly coherent (coherence = 0.68). The increase in force oscillations below 0.5 Hz from 5 to 30% MVC was related to an increase in EMG burst oscillations below 0.5 Hz (R ² = 0.51). Finally, there was a strong association between the increase in EMG burst oscillations below 0.5 Hz and the interference EMG from 35–60 Hz (R ² = 0.95). In conclusion, this finding demonstrates that bursting of the EMG signal contains low-frequency oscillations below 0.5 Hz, which are associated with oscillations in force below 0.5 Hz.     

Estimation of monopolar signals from sphincter muscles and removal of common mode interference

The detection of surface electromyogram (EMG) by multi-electrode systems is applied in many research studies. The signal is usually recorded by means of spatial filters (linear combination of the potential under at least two electrodes) with vanishing sum of weights. Nevertheless, more information could be extracted from monopolar signals measured with respect to a reference electrode away from the muscle. Under certain conditions, surface EMG signal along a curve parallel to the fibre path has zero mean (property approximately satisfied when EMG is sampled by an array of electrodes that covers the entire support of the signal in space). This property allows estimating monopolar from single differential (SD) signals by pseudoinversion of the matrix relating monopolar to SD signals. The method applies to EMG signals from the external anal sphincter muscle, recorded using a specific cylindrical probe with an array of electrodes located along the circular path of the fibres. The performance of the algorithm for the estimation of monopolar from SD signals is tested on simulated signals. The estimation error of monopolar signals decreases by increasing the number of channels. Using at least 12 electrodes, the estimation error is negligible. The method applies to single fibre action potentials, single motor unit action potentials, and interference signals.The same method can also be applied to reduce common mode interference from SD signals from muscles with rectilinear fibres. In this case, the last SD channel defined as the difference between the potentials of the last and the first electrodes must be recorded, so that the sum of all the SD signals vanishes. The SD signals estimated from the double differential signals by pseudoinvertion are free of common mode.     

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

The continuous wavelet transform (CWT), a time-frequency method, was used when calculating mean frequency of the power spectrum (MNF) and signal amplitude (RMS) of the surface EMG to investigate their relationships to force during a gradually increasing knee extension (ramp). Based upon the CWT, MNF was redefined to include time dependence on the EMG signal frequency contents, the short-time MNF (STMNF). Surface EMG was recorded from vastus lateralis, rectus femoris and vastus medialis in 21 clinically healthy subjects during a brief, gradually increasing contraction up to 100% of a maximum voluntary contraction (MVC), with a duration of approximately 10 s. The relationships between the EMG variables and force using linear regression were determined for each subject. For vastus lateralis, we also investigated if certain aspects of the muscle morphology (i.e., proportions and areas of different fibre types) influenced the EMG-force relationship.For the majority of subjects (17-18 out of 21 subjects) there were significant positive correlations between STMNF and force in the three muscles. No sex differences were found in intercepts or regression coefficients of STMNF. The muscle morphology had a significant influence on the STMNF-force intercept and the regression coefficient. Positive and highly significant linear correlations between RMS and force were found for all subjects and all three muscles.In conclusion, time frequency methods can be applied when investigating EMG during brief contractions associated with non-stationarity. In a great majority of the subjects, and in the three muscles, significant linear force dependencies were found for STMNF. Thus, when evaluating muscle fatigue, e.g., in ergonomic situations, it is important to consider the force level as one factor that can influence the results. Morphological variables (fibre proportions and fibre areas) influenced the STMNF-force relationship in vastus lateralis.     

Surface EMG variability while running on grass,concrete and treadmill

This study aimed to investigate whether inter-trial variability in muscle activity (electromyography, EMG) during running is influenced by the number of acquired steps and running surface. Nine healthy participants ran at preferred speed on treadmill, concrete, and grass. Tibial acceleration and surface EMG from 12 lower limb muscles were recorded. The coefficient of variation (CV) from the average EMG and peak EMG were computed from 5, 10, 25, 50 and 100 steps in each running surface. Data average stability was computed using sequential estimation technique (SET) from 100 steps. The CV for average and peak EMG was lower during treadmill running compared to running on grass (−11 ± 2.88%) or concrete (−9 ± 2.94%) (p < 0.05), without differences across the different number of steps. Moreover, the peak EMG CV from peroneus longus was lower on concrete (p < 0.05), whereas gluteus maximus presented greater variability on grass compared to concrete (p < 0.05). The SET analysis revealed that average stability is reached with up to 10 steps across all running conditions. Therefore, treadmill running induced greater variability compared to overground, without influence of the number of steps on EMG variability. Moreover, average stability for EMG recordings may be reached with up to 10 steps.