Innervation zone of the vastus medialis muscle: position and effect on surface EMG variables

Muscle innervation zone estimation from monopolar high-density M-waves using principal component analysis and radon transform

This study examined methods for estimating the innervation zone (IZ) of a muscle using recorded monopolar high density M waves. Two IZ estimation methods based on principal component analysis (PCA) and Radon transform (RT) were examined. Experimental M waves, acquired from the biceps brachii muscles of nine healthy subjects were used as testing data sets. The performance of the two methods was evaluated by comparing their IZ estimations with manual IZ detection by experienced human operators. Compared with manual detection, the agreement rate of the estimated IZs was 83% and 63% for PCA and RT based methods, respectively, both using monopolar high density M waves. In contrast, the agreement rate was 56% for cross correlation analysis using bipolar high density M waves. The mean difference in estimated IZ location between manual detection and the tested method was 0.12 ± 0.28 inter-electrode-distance (IED) for PCA, 0.33 ± 0.41 IED for RT and 0.39 ± 0.74 IED for cross correlation-based methods. The results indicate that the PCA based method was able to automatically detect muscle IZs from monopolar M waves. Thus, PCA provides an alternative approach to estimate IZ location of voluntary or electrically-evoked muscle contractions, and may have particular value for IZ detection in patients with impaired voluntary muscle activation.

Neurophysiological Factors Affecting Muscle Innervation Zone Estimation Using Surface EMG: A Simulation Study

Surface electromyography (EMG) recorded by a linear or 2-dimensional electrode array can be used to estimate the location of muscle innervation zones (IZ). There are various neurophysiological factors that may influence surface EMG and thus potentially compromise muscle IZ estimation. The objective of this study was to evaluate how surface-EMG-based IZ estimation might be affected by different factors, including varying degrees of motor unit (MU) synchronization in the case of single or double IZs. The study was performed by implementing a model simulating surface EMG activity. Three different MU synchronization conditions were simulated, namely no synchronization, medium level synchronization, and complete synchronization analog to M wave. Surface EMG signals recorded by a 2-dimensional electrode array were simulated from a muscle with single and double IZs, respectively. For each situation, the IZ was estimated from surface EMG and compared with the one used in the model for performance evaluation. For the muscle with only one IZ, the estimated IZ location from surface EMG was consistent with the one used in the model for all the three MU synchronization conditions. For the muscle with double IZs, at least one IZ was appropriately estimated from interference surface EMG when there was no MU synchronization. However, the estimated IZ was different from either of the two IZ locations used in the model for the other two MU synchronization conditions. For muscles with a single IZ, MU synchronization has little effect on IZ estimation from electrode array surface EMG. However, caution is required for multiple IZ muscles since MU synchronization might lead to false IZ estimation.

Human myoelectric spatial patterns differ among lower limb muscles and locomotion speeds

The spatial distribution of myoelectric activity within lower limb muscles is often nonuniform and can change during different stationary tasks. Recent studies using high-density electromyography (EMG) have suggested that spatial muscle activity may also differ among muscles during locomotion, but contrasting electrode array sizes and experimental designs have limited cross-study comparisons. Here, we sought to determine if spatial EMG patterns differ among lower limb muscles and locomotion speeds. We recorded high-density EMG from the vastus medialis, tibialis anterior, biceps femoris, medial gastrocnemius, and lateral gastrocnemius muscles of 11 healthy subjects while they walked (1.2 and 1.6 m/s) and ran (2.0, 3.0, 4.0, and 5.0 m/s) on a treadmill. To overcome the detrimental effects of cable, electrode, and soft tissue movements on high-density EMG signal quality during locomotion, we applied multivariate signal cleaning methods. From these data, we computed the spatial entropy and center of gravity from the total myoelectric activity within each recording array during the stance or swing phases of the gait cycle. We found heterogeneous spatial EMG patterns evidenced by contrasting spatial entropy among lower limb muscles. As locomotion speed increased, mean entropy values decreased in four of the five recorded muscles, indicating that EMG signal amplitudes were more spatially heterogeneous, or localized, at faster speeds. The EMG center of gravity location also shifted in multiple muscles as locomotion speed increased. Contrasting myoelectric spatial distributions among muscles likely reflect differences in muscle architecture, but increasingly localized activity and spatial shifts in the center of gravity location at faster locomotion speeds could be influenced by preferential recruitment of faster motor units under greater loads.

Changes in supramaximal M-wave amplitude at different regions of biceps brachii following eccentric exercise of the elbow flexors

PURPOSE

Previous evidence from surface electromyograms (EMGs) suggests that exercise-induced muscle damage (EIMD) may manifest unevenly within the muscle. Here we investigated whether these regional changes were indeed associated with EIMD or if they were attributed to spurious factors often affecting EMGs.

METHODS

Ten healthy male subjects performed 3 × 10 eccentric elbow flexions. Maximal voluntary contraction (MVC), muscle soreness and ultrasound images from biceps brachii distal and proximal regions were measured immediately before (baseline) and during each of the following 4 days after the exercise. Moreover, 64 monopolar surface EMGs were detected while 10 supramaximal pulses were applied to the musculocutaneous nerve. The innervation zone (IZ), the number of electrodes detecting largest M-waves and their centroid longitudinal coordinates were assessed to characterize the spatial distribution of the M-waves amplitude.

RESULTS

The MVC torque decreased (~ 25%; P < 0.001) while the perceived muscle soreness scale increased (~ 4 cm; 0 cm for no soreness and 10 cm for highest imaginable soreness; P < 0.005) across days. The echo intensity of the ultrasound images increased at 48 h (71%), 72 h (95%) and 96 h (112%) for both muscle regions (P < 0.005), while no differences between regions were observed (P = 0.136). The IZ location did not change (P = 0.283). The number of channels detecting the greatest M-waves significantly decreased (up to 10.7%; P < 0.027) and the centroid longitudinal coordinate shifted distally at 24, 48 and 72 h after EIMD (P < 0.041).

CONCLUSION

EIMD consistently changed supramaximal M-waves that were detected mainly proximally from the biceps brachii, suggesting that EIMD takes place locally within the biceps brachii.

Global Innervation Zone Identification With High-Density Surface Electromyography

OBJECTIVE

The aim of this study is to compare the performance of three strategies in determining the global innervation zone (IZ) distribution.

METHODS

High-density surface electromyography was recorded from the biceps brachii muscle of seven healthy subjects under isometric voluntary contractions at 20%, 50%, and 100% of the maximal voluntary contraction and supramaximal musculocutaneous nerve stimulations. IZs were detected: first, by visual identification in a column-specific manner (IZ-1D); second, based on decomposed bipolar mapping of motor unit action potentials (IZ-2D); and third, by source imaging in the three-dimensional muscle space (IZ-3D).

RESULTS

All three IZ detection approaches have exhibited excellent trial-to-trial repeatability. Consistent IZ results were found in the axial direction of the arm across all three approaches, yet a difference was observed in the mediolateral direction.

CONCLUSIONS

Among all three approaches, IZ-3D is capable of providing the most comprehensive information regarding the global IZ distribution, while maintaining high consistency with IZ-1D and IZ-2D results.

SIGNIFICANCE

IZ-3D approach can be a potential tool for global IZ imaging, which is critical to the clinical diagnosis and treatment of neuromuscular disorders.

Regional Vastus Medialis and Vastus Lateralis Activation in Females with Patellofemoral Pain

INTRODUCTION

This study aimed to investigate whether regional activation patterns in the vasti muscles differ between females with and without patellofemoral pain (PFP), and whether muscle activation patterns correlate with knee extension strength.

METHODS

Thirty-six females with PFP and 20 pain-free controls performed a standardized knee flexion-extension task. The activation of vastus medialis (VM) and vastus lateralis (VL) was collected using high-density surface EMG and analyzed using principal component (PC) analysis. Spatial locations and temporal coefficients of the PC, and the percent variance they explain, were compared between groups and between the concentric and the eccentric phases of the movement. Correlations were assessed between PC features and knee extension strength.

RESULTS

The spatial weights of PC1 (general vasti activation) and PC2 (reflecting vastus-specific activation) were similar between groups (R > 0.95). Activation patterns in PFP were less complex than controls. Fewer PC features were necessary to reconstruct 90% of the signal for PFP participants in the concentric phase (P < 0.05), and the difference in bias of activation to VM (concentric phase) or VL (eccentric phase) was less between phases for PFP participants (P < 0.05). Smaller difference in vastus-specific activation in concentric and eccentric phases (less task specificity of VM/VL coordination) was related to greater maximal knee extension strength (P < 0.05, R < -0.43).

CONCLUSION

These data suggest PFP involves a simpler control strategy of VM and VL. The inverse association between task specificity and maximal knee extension strength suggests different presentations of PFP: lower knee extension strength but VM/VL coordination task specificity comparable with controls, or knee extension strength comparable with controls but lower VM/VL coordination task specificity.

Intermuscular Coherence Between Surface EMG Signals Is Higher for Monopolar Compared to Bipolar Electrode Configurations

Introduction: The vasti muscles have to work in concert to control knee joint motion during movements like walking, running, or squatting. Coherence analysis between surface electromyography (EMG) signals is a common technique to study muscle synchronization during such movements and gain insight into strategies of the central nervous system to optimize neuromuscular performance. However, different assessment methods related to EMG data acquisition, e.g., different electrode configurations or amplifier technologies, have produced inconsistent observations. Therefore, the aim of this study was to elucidate the effect of different EMG acquisition techniques (monopolar vs. bipolar electrode configuration, potential vs. current amplifier) on the magnitude, reliability, and sensitivity of intermuscular coherence between two vasti muscles during stable and unstable squatting exercises.

Methods: Surface EMG signals from vastus lateralis (VL) and medialis (VM) were obtained from eighteen adults while performing series of stable und unstable bipedal squats. The EMG signals were acquired using three different recording techniques: (1) Bipolar with a potential amplifier, (2) monopolar with a potential amplifier, and (3) monopolar electrodes with a current amplifier. VL-VM coherence between the respective raw EMG signals was determined during two trials of stable squatting and one trial of unstable squatting to compare the coherence magnitude, reliability, and sensitivity between EMG recording techniques.

Results: VL-VM coherence was about twice as high for monopolar recordings compared to bipolar recordings for all squatting exercises while coherence was similar between monopolar potential and current recordings. Reliability measures were comparable between recording systems while the sensitivity to an increase in intermuscular coherence during unstable vs. stable squatting was lowest for the monopolar potential system.

Discussion and Conclusion: The choice of electrode configuration can have a significant effect on the magnitude of EMG-EMG coherence, which may explain previous inconsistencies in the literature. A simple simulation of cross-talk could not explain the large differences in intermuscular coherence. It is speculated that inevitable errors in the alignment of the bipolar electrodes with the muscle fiber direction leads to a reduction of information content in the differential EMG signals and subsequently to a lower resolution for the detection of intermuscular coherence.

Identification of regional activation by factorization of high-density surface EMG signals: A comparison of Principal Component Analysis and Non-negative Matrix factorization

In this study, we investigated whether principal component analysis (PCA) and non-negative matrix factorization (NMF) perform similarly for the identification of regional activation within the human vastus medialis. EMG signals from 64 locations over the VM were collected from twelve participants while performing a low-force isometric knee extension. The envelope of the EMG signal of each channel was calculated by low-pass filtering (8 Hz) the monopolar EMG signal after rectification. The data matrix was factorized using PCA and NMF, and up to 5 factors were considered for each algorithm. Association between explained variance, spatial weights and temporal scores between the two algorithms were compared using Pearson correlation. For both PCA and NMF, a single factor explained approximately 70% of the variance of the signal, while two and three factors explained just over 85% or 90%. The variance explained by PCA and NMF was highly comparable (R > 0.99). Spatial weights and temporal scores extracted with non-negative reconstruction of PCA and NMF were highly associated (all p < 0.001, mean R > 0.97). Regional VM activation can be identified using high-density surface EMG and factorization algorithms. Regional activation explains up to 30% of the variance of the signal, as identified through both PCA and NMF.

Effect of electrode position of low intensity neuromuscular electrical stimulation on the evoked force in the quadriceps femoris muscle

Objective

The present study aimed to test the effect of the electrode position and inter-electrode distance on the evoked force by neuromuscular electrical stimulation (NMES) with a low current intensity and a single pair of electrodes. Knee extensor forces during NMES to quadriceps femoris muscles were compared among four different electrode configurations in seven healthy men. Electrodes were located at 10 cm proximal and 15 cm distal (P10-D15), 10 cm proximal and 10 cm distal (P10-D10), 5 cm proximal and 15 cm distal, and 5 cm proximal and 10 cm distal (P5-D10) to the center of the longitudinal axis of the quadriceps femoris muscles.

Results

The evoked force–time area for P5-D10 was significantly higher than those for P10-D15 and P10-D10 (p < 0.05). When using NMES devices with a low current intensity, a shorter inter-electrode distance and relatively distal locations can promote greater evoked forces in the quadriceps femoris muscles.

Detection of Multiple Innervation Zones from Multi-Channel Surface EMG Recordings with Low Signal-to-Noise Ratio Using Graph-Cut Segmentation

Knowledge of the location of muscle Innervation Zones (IZs) is important in many applications, e.g. for minimizing the quantity of injected botulinum toxin for the treatment of spasticity or for deciding on the type of episiotomy during child delivery. Surface EMG (sEMG) can be noninvasively recorded to assess physiological and morphological characteristics of contracting muscles. However, it is not often possible to record signals of high quality. Moreover, muscles could have multiple IZs, which should all be identified. We designed a fully-automatic algorithm based on the enhanced image Graph-Cut segmentation and morphological image processing methods to identify up to five IZs in 60-ms intervals of very-low to moderate quality sEMG signal detected with multi-channel electrodes (20 bipolar channels with Inter Electrode Distance (IED) of 5 mm). An anisotropic multilayered cylinder model was used to simulate 750 sEMG signals with signal-to-noise ratio ranging from -5 to 15 dB (using Gaussian noise) and in each 60-ms signal frame, 1 to 5 IZs were included. The micro- and macro- averaged performance indices were then reported for the proposed IZ detection algorithm. In the micro-averaging procedure, the number of True Positives, False Positives and False Negatives in each frame were summed up to generate cumulative measures. In the macro-averaging, on the other hand, precision and recall were calculated for each frame and their averages are used to determine F1-score. Overall, the micro (macro)-averaged sensitivity, precision and F1-score of the algorithm for IZ channel identification were 82.7% (87.5%), 92.9% (94.0%) and 87.5% (90.6%), respectively. For the correctly identified IZ locations, the average bias error was of 0.02±0.10 IED ratio. Also, the average absolute conduction velocity estimation error was 0.41±0.40 m/s for such frames. The sensitivity analysis including increasing IED and reducing interpolation coefficient for time samples was performed. Meanwhile, the effect of adding power-line interference and using other image interpolation methods on the deterioration of the performance of the proposed algorithm was investigated. The average running time of the proposed algorithm on each 60-ms sEMG frame was 25.5±8.9 (s) on an Intel dual-core 1.83 GHz CPU with 2 GB of RAM. The proposed algorithm correctly and precisely identified multiple IZs in each signal epoch in a wide range of signal quality and is thus a promising new offline tool for electrophysiological studies.

Regional activation within the vastus medialis in stimulated and voluntary contractions

This study examined the contribution of muscle fiber orientation at different knee angles to regional activation identified with high-density surface electromyography (HDsEMG). Monopolar HDsEMG signals were collected using a grid of 13 × 5 electrodes placed over the vastus medialis (VM). Intramuscular electrical stimulation was used to selectively activate two regions within VM. The distribution of EMG responses to stimulation was obtained by calculating the amplitude of the compound action potential for each channel; the position of the peak amplitude was tracked across knee angles to describe shifts of the active muscle regions under the electrodes. In a separate experiment, regional activation was investigated in 10 knee flexion-extension movements against a fixed resistance. Intramuscular stimulation of different VM regions resulted in clear differences in amplitude distribution along the columns of the electrode grid (P < 0.001); changes in knee angle resulted in consistent shifts along the rows (P < 0.01) and negligible shifts along the columns of the electrode grid. Regional VM activation was identified in dynamic movement, with distal shifts of the EMG distribution in the eccentric phase of the movement (P < 0.05) and at more flexed knee angles (P < 0.05). HDsEMG was used to describe regional activation across the VM that was not attributable to anatomic factors. Changes in muscle fiber orientation associated with knee joint angle mainly influence the amplitude distribution along the fiber direction. Future studies are needed to understand possible functional roles for regional activation within the VM in dynamic tasks.

Territory and fiber orientation of vastus medialis motor units: A Surface electromyography investigation

INTRODUCTION

The aim of this study was to determine whether muscle fibers innervated by single motor neurons are confined in small subvolumes of the vastus medialis (VM) and if motor unit fiber orientation depends on their position within the muscle.

METHODS

Single motor units were identified from a grid of surface electrodes. The size of their surface representation and fiber orientation were extracted using an algorithm validated on simulated signals.

RESULTS

The action potentials of 77 motor units were represented locally on the skin (10th-90th percentiles: 14-25 mm). According to simulations, this indicates territories smaller than 11.8-64.8 mm. Motor units in distal regions of VM had fibers at a greater angle than those in proximal regions (R = -0.54, P < 0.001).

CONCLUSION

Motor units with small territories and varying fiber orientations may be an anatomical predisposition to regulate how regions within VM apply forces to the patella. This could help to redistribute loads within the joint in painful conditions.

The role of muscle synergies in myoelectric control: trends and challenges for simultaneous multifunction control

Myoelectric control is filled with potential to significantly change human-robot interaction due to the ability to non-invasively measure human motion intent. However, current control schemes have struggled to achieve the robust performance that is necessary for use in commercial applications. As demands in myoelectric control trend toward simultaneous multifunctional control, multi-muscle coordinations, or synergies, play larger roles in the success of the control scheme. Detecting and refining patterns in muscle activations robust to the high variance and transient changes associated with surface electromyography is essential for efficient, user-friendly control. This article reviews the role of muscle synergies in myoelectric control schemes by dissecting each component of the scheme with respect to associated challenges for achieving robust simultaneous control of myoelectric interfaces. Electromyography recording details, signal feature extraction, pattern recognition and motor learning based control schemes are considered, and future directions are proposed as steps toward fulfilling the potential of myoelectric control in clinically and commercially viable applications.