F wave analysis based on the compound muscle action potential scan.

INTRODUCTION/AIMS: Conventional F wave analysis involves a relatively uniform physiological environment induced by supramaximal stimulations. The F wave characteristics in a dynamic physiological condition, however, are rarely investigated. This study aimed to improve understanding of F wave properties in the more dynamic process by introducing a novel method to analyze F waves based on the compound muscle action potential (CMAP) scan technique. METHODS: Twenty four healthy subjects participated in the study. The CMAP scan was applied to record muscle responses in the abductor pollicis brevis (APB) and abductor digiti minimi (ADM) muscles, respectively. F wave characteristics including mean F wave amplitude and latency (F-M latency), persistence and activating threshold were quantified. RESULTS: An average of 200 F waves per muscle were obtained from the CMAP scan recording. Weak to moderate correlations between F wave amplitude and stimulating intensity were observed in most of the APB (19 muscles; r = 0.33 ± 0.14, all p < .05) and ADM (23 muscles, r = 0.46 ± 0.16, all p < .05) muscles. Significantly longer mean F latency and lower activating F-threshold were found in the ADM muscles (F-M latency: APB: 25.43 ± 2.39 ms, ADM: 26.15 ± 2.32 ms, p < .05; F-threshold: APB: 7.68 ± 8.96% CMAP, ADM: 2.35 ± 2.42% CMAP, p < .05). DISCUSSION: This study introduces new features of F waves using the CMAP scan technique and identifies differences of F wave characteristics between the hand muscles. The CMAP scan based F waves analysis can be combined with the motor unit number estimation to assess functional alterations in motor neurons in neurological disorders.

MScanFit motor unit number estimation of human anconeus muscle.

INTRODUCTION/AIMS: Motor unit number estimation (MUNE) studies of the anconeus muscle are very limited, although the information they provide is useful for neurophysiological investigations. The objective of this study was to estimate the number of motor units in the anconeus muscle. METHODS: Compound muscle action potential scans of the anconeus muscle were recorded from 11 healthy participants, all of whom were tested on two occasions. MUNE was determined from the MScanFit program. RESULTS: The average MUNE of the anconeus muscle was 55.09 ± 3.27 (mean ± standard error of the mean) for the first test and 54.64 ± 3.70 for the retest, demonstrating excellent measurement reliability, with an intraclass correlation coefficient of 0.90. DISCUSSION: A relatively low motor unit number is found in the anconeus, a muscle not comprehensively studied in literature.

First-recruited motor units adopt a faster phenotype in amyotrophic lateral sclerosis.

KEY POINTS: Amyotrophic lateral sclerosis (ALS) is an incurable neurodegenerative disorder of motor neurons, carrying a short survival. High-density motor unit recordings permit analysis of motor unit size (amplitude) and firing behaviour (afterhyperpolarization duration and muscle fibre conduction velocity). Serial recordings from biceps brachii indicated that motor units fired faster and with greater amplitude as disease progressed. First-recruited motor units in the latter stages of ALS developed characteristics akin to fast-twitch motor units, possibly as a compensatory mechanism for the selective loss of this motor unit subset. This process may become maladaptive, highlighting a novel therapeutic target to reduce motor unit vulnerability. ABSTRACT: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder with a median survival of 3 years. We employed serial high-density surface electromyography (HDSEMG) to characterize voluntary and ectopic patterns of motor unit (MU) firing at different stages of disease. By distinguishing MU subtypes with variable vulnerability to disease, we aimed to evaluate compensatory neuronal adaptations that accompany disease progression. Twenty patients with ALS and five patients with benign fasciculation syndrome (BFS) underwent 1-7 assessments each. HDSEMG measurements comprised 30 min of resting muscle and 1 min of light voluntary activity from biceps brachii bilaterally. MU decomposition was performed by the progressive FastICA peel-off technique. Inter-spike interval, firing pattern, MU potential area, afterhyperpolarization duration and muscle fibre conduction velocity were determined. In total, 373 MUs (ALS = 287; BFS = 86) were identified from 182 recordings. Weak ALS muscles demonstrated a lower mean inter-spike interval (82.7 ms) than strong ALS muscles (96.0 ms; P = 0.00919) and BFS muscles (95.3 ms; P = 0.0039). Mean MU potential area (area under the curve: 487.5 vs. 98.7 µV ms; P < 0.0001) and muscle fibre conduction velocity (6.2 vs. 5.1 m/s; P = 0.0292) were greater in weak ALS muscles than in BFS muscles. Purely fasciculating MUs had a greater mean MU potential area than MUs also under voluntary command (area under the curve: 679.6 vs. 232.4 µV ms; P = 0.00144). These results suggest that first-recruited MUs develop a faster phenotype in the latter stages of ALS, likely driven by the preferential loss of vulnerable fast-twitch MUs. Inhibition of this potentially maladaptive phenotypic drift may protect the longevity of the MU pool, stimulating a novel therapeutic avenue.

Model-Based Analysis of Muscle Strength and EMG-Force Relation with respect to Different Patterns of Motor Unit Loss.

This study presents a model-based sensitivity analysis of the strength of voluntary muscle contraction with respect to different patterns of motor unit loss. A motor unit pool model was implemented including simulation of a motor neuron pool, muscle force, and surface electromyogram (EMG) signals. Three different patterns of motor unit loss were simulated, including (1) motor unit loss restricted to the largest ones, (2) motor unit loss restricted to the smallest ones, and (3) motor unit loss without size restriction. The model outputs including muscle force amplitude, variability, and the resultant EMG-force relation were quantified under two different motor neuron firing strategies. It was found that motor unit loss restricted to the largest ones had the most dominant impact on muscle strength and significantly changed the EMG-force relation, while loss restricted to the smallest motor units had a pronounced effect on force variability. These findings provide valuable insight toward our understanding of the neurophysiological mechanisms underlying experimental observations of muscle strength, force control, and EMG-force relation in both normal and pathological conditions.

FastICA peel-off for ECG interference removal from surface EMG.

BACKGROUND: Multi-channel recording of surface electromyographyic (EMG) signals is very likely to be contaminated by electrocardiographic (ECG) interference, specifically when the surface electrode is placed on muscles close to the heart. METHODS: A novel fast independent component analysis (FastICA) based peel-off method is presented to remove ECG interference contaminating multi-channel surface EMG signals. Although demonstrating spatial variability in waveform shape, the ECG interference in different channels shares the same firing instants. Utilizing the firing information estimated from FastICA, ECG interference can be separated from surface EMG by a "peel off" processing. The performance of the method was quantified with synthetic signals by combining a series of experimentally recorded "clean" surface EMG and "pure" ECG interference. RESULTS: It was demonstrated that the new method can remove ECG interference efficiently with little distortion to surface EMG amplitude and frequency. The proposed method was also validated using experimental surface EMG signals contaminated by ECG interference. CONCLUSIONS: The proposed FastICA peel-off method can be used as a new and practical solution to eliminating ECG interference from multichannel EMG recordings.

Progressive FastICA Peel-Off and Convolution Kernel Compensation Demonstrate High Agreement for High Density Surface EMG Decomposition.

Decomposition of electromyograms (EMG) is a key approach to investigating motor unit plasticity. Various signal processing techniques have been developed for high density surface EMG decomposition, among which the convolution kernel compensation (CKC) has achieved high decomposition yield with extensive validation. Very recently, a progressive FastICA peel-off (PFP) framework has also been developed for high density surface EMG decomposition. In this study, the CKC and PFP methods were independently applied to decompose the same sets of high density surface EMG signals. Across 91 trials of 64-channel surface EMG signals recorded from the first dorsal interosseous (FDI) muscle of 9 neurologically intact subjects, there were a total of 1477 motor units identified from the two methods, including 969 common motor units. On average, 10.6 ± 4.3 common motor units were identified from each trial, which showed a very high matching rate of 97.85 ± 1.85% in their discharge instants. The high degree of agreement of common motor units from the CKC and the PFP processing provides supportive evidence of the decomposition accuracy for both methods. The different motor units obtained from each method also suggest that combination of the two methods may have the potential to further increase the decomposition yield.

2CFastICA: A Novel Method for High Density Surface EMG Decomposition Based on Kernel Constrained FastICA and Correlation Constrained FastICA.

This study presents a novel high density surface electromyography (EMG) decomposition method, named as 2CFastICA, because it incorporates two key algorithms: kernel constrained FastICA and correlation constrained FastICA. The former focuses on overcoming the local convergence of FastICA without requiring the peel-off strategy used in the progressive FastICA peel-off (PFP) framework. The latter further refines the output of kernel constrained FastICA by correcting possible erroneous or missed spikes. The two constrained FastICA algorithms supplement each other to warrant the decomposition performance. The 2CFastICA method was validated using simulated surface EMG signals with different motor unit numbers and signal to noise ratios (SNRs). Two source validation was also performed by simultaneous high density surface EMG and intramuscular EMG recordings, showing a matching rate (MR) of (97.2 ± 3.5)% for 170 common motor units. In addition, a different form of two source validation was also conducted taking advantages of the high density surface EMG characteristics of patients with amyotrophic lateral sclerosis, showing a MR of (99.4 ± 0.9)% for 34 common motor units from interference and sparse datasets. Both simulation and experimental results indicate that 2CFastICA can achieve similar decomposition performance to PFP. However, the efficiency of decomposition can be greatly improved by 2CFastICA since the complex signal processing procedures associated with the peel-off strategy are not required any more. Along with this paper, we also provide the MATLAB open source code of 2CFastICA for high density surface EMG decomposition.

Online Decomposition of Surface Electromyogram Into Individual Motor Unit Activities Using Progressive FastICA Peel-Off.

Surface electromyogram (SEMG) decomposition provides a promising tool for decoding and understanding neural drive information non-invasively. In contrast to previous SEMG decomposition methods mainly developed in offline conditions, there are few studies on online SEMG decomposition. A novel method for online decomposition of SEMG data is presented using the progressive FastICA peel-off (PFP) method. The proposed online method utilized a two-stage approach, consisting of an offline prework stage for initializing high-quality separation vectors through the offline PFP algorithm, and an online decomposition stage for estimating source signals of different motor units by applying these vectors to the input SEMG data stream. Specifically, a new successive multi-threshold Otsu algorithm was developed in the online stage for determining each motor unit spike train (MUST) precisely with fast and simple computations (to replace a time-consuming iterative threshold setting in the original PFP method). The performance of the proposed online SEMG decomposition method was evaluated by both simulation and experimental approaches. When processing simulated SEMG data, the online PFP method achieved a decomposition accuracy of 97.37%, superior to that (95.1%) of an online method with a traditional k-means clustering algorithm for MUST extraction. Our method was also found to achieve superior performance at higher noise levels. For decomposing experimental SEMG data, the online PFP method was able to extract an average of 12.00 ± 3.46 MUs per trial, with a matching rate of 90.38%, with respect to the expert-guided offline decomposition results. Our study provides a valuable way of online decomposition of SEMG data with advanced applications in movement control and health.

Adaptive Online Decomposition of Surface EMG Using Progressive FastICA Peel-Off.

This study presents a method for adaptive online decomposition of high-density surface electromyogram (SEMG) signals to overcome the performance degradation during long-term recordings. The proposed method utilized the progressive FastICA peel-off (PFP) method and integrated a practical double-thread-parallel algorithm into the conventional two-stage calculation approach. During the offline initialization stage, a set of separation vectors was computed. In the subsequent online decomposition stage, a backend thread was implemented to periodically update the separation vectors using the constrained FastICA algorithm and the automatic PFP method. Concurrently, the frontend thread employed the newly updated separation vectors to accurately extract motor unit (MU) spike trains in real time. To assess the effectiveness of the proposed method, simulated and experimental SEMG signals from abductor pollicis brevis muscles of ten subjects were used for evaluation. The results demonstrated that the proposed method outperformed the conventional method, which relies on fixed separation vectors. Specifically, the proposed method showed an improved matching rate by 3.63% in simulated data and 1.98% in experimental data, along with an increased motor unit number by 2.39 in simulated data and 1.30 in experimental data. These findings illustrated the feasibility of the proposed method to enhance the performance of online SEMG decomposition. As a result, this work holds promise for various applications that require accurate MU firing activities in decoding neural commands and building neural-machine interfaces.

A Novel Analysis of CMAP Scans From Perspective of Information Theory: CMAP Distribution Index (CDIX).

OBJECTIVE: The compound muscle action potential (CMAP) scan is a useful technique for examination of neuromuscular disorders. The objective of this study is to develop a novel analysis of CMAP scans from the perspective of information theory. METHODS: A novel index parameter called CMAP distribution index (CDIX) was developed to characterize CMAP scan based on calculation of the information entropy. The performance of CDIX was evaluated using CMAP scan data from healthy control and spinal cord injury (SCI) subjects, and compared with D50 and MScanFit motor unit number estimation (MUNE). RESULTS: CDIX was significantly lower for the SCI subjects compared with the healthy control subjects (p < 0.001). A significant correlation ( R2 = 0.58, p < 0.001) was found between CDIX and MScanFit MUNE. Among all tested parameters (maximum CMAP, D50, MScanFit MUNE and CDIX), CDIX achieved the smallest relative width of the overlapping zone (WOZ%) between SCI and healthy control subjects. CONCLUSION: CDIX can be inferred as a useful index reflecting motor unit loss and muscle fiber reinnervation changes.

A Novel Analysis of Compound Muscle Action Potential Scan: Staircase Function Fitting and StairFit Motor Unit Number Estimation.

Compound muscle action potential (CMAP) scan provides a detailed stimulus-response curve for examination of neuromuscular disease. The objective of the study is to develop a novel CMAP scan analysis to extract motor unit number estimation (MUNE) and other physiological or diagnostic information. A staircase function was used as the basic mathematical model of the CMAP scan. An optimal staircase function fitting model was estimated for each given number of motor units, and the fitting model with the minimum number of motor units that meets a predefined error requirement was accepted. This yields MUNE as well as the spike amplitude and activation threshold of each motor unit that contributes to the CMAP scan. The significance of the staircase function fit was confirmed using simulated CMAP scans with different motor unit number (20, 50, 100 and 150) and baseline noise (1 µV, 5 µV and 10 µV) inputs, in terms of MUNE performance, repeatability, and the test-retest reliability. For experimental data, the average MUNE of the first dorsal interosseous muscle derived from the staircase function fitting was 57.5 ± 26.9 for the tested spinal cord injury subjects, which was significantly lower than 101.2 ± 16.9, derived from the control group (p < 0.001). The staircase function fitting provides an appropriate approach to CMAP scan processing, yielding MUNE and other useful parameters for examination of motor unit loss and muscle fiber reinnervation.

Sensitivity Analysis of CMAP Scan Step Index to Different Stimulation Parameters and Examination of Muscles Affected by Spinal Cord Injury.

Step index (STEPIX) is a recently developed compound muscle action potential (CMAP) scan method for evaluating motor unit loss and remodeling changes. This study investigates the influence of different stimulation parameters during CMAP scan on STEPIX and its examination of muscles affected by spinal cord injury (SCI). CMAP scan of the first dorsal interosseous (FDI) muscle was performed using different stimulus pulse widths (0.1 ms, 0.2 ms) and different numbers of stimuli (500, 1000) in 12 neurologically intact subjects. STEPIX was derived from each CMAP scan of all subjects. A significantly higher STEPIX was obtained using 1000 stimuli than 500 stimuli, while no significant difference in STEPIX was observed using 0.1 and 0.2 ms stimulus pulse widths. STEPIX was further applied to process CMAP scans of the FDI muscle from 13 tetraplegia and 13 healthy control subjects using the same stimulation parameter setting (0.1 ms, 500 stimuli), along with other methods including MScanFit motor unit number estimation (MUNE) and D50. STEPIX was significantly lower for the SCI subjects compared with the healthy control subjects. STEPIX was significantly correlated with MscanFit MUNE and D50, but had a smaller relative width of the overlapping zone (WOZ%) between tetraplegic and healthy control groups compared with MScanFit MUNE and D50. The findings of the study highlight the importance of maintaining a consistent stimulation parameter setting in CMAP scan studies and confirm the usefulness of STEPIX as a convenient CMAP scan parameter for examination of motor unit number changes.

Spatial Dependence of Log-Transformed Electromyography-Force Relation: Model-Based Sensitivity Analysis and Experimental Study of Biceps Brachii.

This study investigated electromyography (EMG)-force relations using both simulated and experimental approaches. A motor neuron pool model was first implemented to simulate EMG-force signals, focusing on three different conditions that test the effects of small or large motor units located more or less superficially in the muscle. It was found that the patterns of the EMG-force relations varied significantly across the simulated conditions, quantified by the slope (b) of the log-transformed EMG-force relation. b was significantly higher for large motor units, which were preferentially located superficially rather than for random depth or deep depth conditions (p < 0.001). The log-transformed EMG-force relations in the biceps brachii muscles of nine healthy subjects were examined using a high-density surface EMG. The slope (b) distribution of the relation across the electrode array showed a spatial dependence; b in the proximal region was significantly larger than the distal region, whereas b was not different between the lateral and medial regions. The findings of this study provide evidence that the log-transformed EMG-force relations are sensitive to different motor unit spatial distributions. The slope (b) of this relation may prove to be a useful adjunct measure in the investigation of muscle or motor unit changes associated with disease, injury, or aging.

Electrophysiological assessment of radial shock wave therapy for carpal tunnel syndrome.

This study presents an electrophysiological assessment of radial extracorporeal shock wave therapy on patients with carpal tunnel syndrome (CTS). Sixteen CTS subjects received radial extracorporeal shock wave therapy once a week for five consecutive weeks. Outcome performance was assessed using the Boston Carpal Tunnel Questionnaire (BCTQ) and electrodiagnostic measurements including a nerve conduction study of the median nerve and a compound muscle action potential (CMAP) scan of the abductor pollicis brevis muscle. The BCTQ and the sensory conduction test measurements were all statistically improved after the treatment. However, the motor conduction test measurements were not significantly different before and after the treatment. The CMAP scan examination revealed MScanFit motor unit number estimation (MUNE) was significantly higher after the treatment, while no significant change was found in StairFit MUNE and step index. These results confirmed the effectiveness of shock wave therapy for treating CTS symptoms and the associated sensory property changes. The reasons for the inconsistencies from different CMAP scan processing methods are worthwhile targets for further investigation.

A Novel Muscle Innervation Zone Estimation Method Using Monopolar High Density Surface Electromyography.

This study presents a novel method to estimate a muscle's innervation zone (IZ) location from monopolar high density surface electromyography (EMG) signals. Based on the fact that 2nd principal component coefficients derived from principal component analysis (PCA) are linearly related with the time delay of different channels, the channels located near the IZ should have the shortest time delays. Accordingly, we applied a novel method to estimate a muscle's IZ based on PCA. The performance of the developed method was evaluated by both simulation and experimental approaches. The method based on 2nd principal component of monopolar high density surface EMG achieved a comparable performance to cross-correlation analysis of bipolar signals when noise was simulated to be independently distributed across all channels. However, in simulated conditions of specific channel contamination, the PCA based method achieved superior performance than the cross-correlation method. Experimental high density surface EMG was recorded from the biceps brachii of 9 healthy subjects during maximum voluntary contractions. The PCA and cross-correlation based methods achieved high agreement, with a difference in IZ location of 0.47 ± 0.4 IED (inter-electrode distance = 8 mm). The results indicate that analysis of 2nd principal component coefficients provides a useful approach for IZ estimation using monopolar high density surface EMG.

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.

Compound muscle action potential (CMAP) scan examination of paretic and contralateral muscles reveals motor unit alterations after stroke.

This study presents a novel compound muscle action potential (CMAP) examination of motor unit changes in paretic muscle post stroke. CMAP scan of the first dorsal interosseous (FDI) muscle was performed bilaterally in 16 chronic stroke subjects. Various parameters were derived from the CMAP scan to examine paretic muscle changes, including CMAP amplitude, D50, step index (STEPIX) and amplitude index (AMPIX). A significant decrease in CMAP amplitude and STEPIX was observed in paretic muscles compared with contralateral muscles (CMAP amplitude: paretic (9.0±0.5) mV, contralateral (11.3±0.9) mV, P=0.024; STEPIX: paretic 101.2±7.6, contralateral 121.9±6.5, P=0.020). No significant difference in D50 and AMPIX was observed between the paretic and contralateral sides (P>0.05). The findings revealed complex paretic muscle changes including motor unit degeneration, muscle fiber denervation, reinnervation and atrophy, providing useful insights to help understand neuromuscular mechanisms associated with weakness and other functional deterioration post stroke. The CMAP scan experimental protocols and the applied processing methods are noninvasive, convenient, and automated, offering practical benefits for clinical application.

A Novel Analysis of Compound Muscle Action Potential Scan: Staircase Function Fitting and StairFit Motor Unit Number Estimation.

Compound muscle action potential (CMAP) scan provides a detailed stimulus-response curve for examination of neuromuscular disease. The objective of the study is to develop a novel CMAP scan analysis to extract motor unit number estimation (MUNE) and other physiological or diagnostic information. A staircase function was used as the basic mathematical model of the CMAP scan. An optimal staircase function fitting model was estimated for each given number of motor units, and the fitting model with the minimum number of motor units that meets a predefined error requirement was accepted. This yields MUNE as well as the spike amplitude and activation threshold of each motor unit that contributes to the CMAP scan. The significance of the staircase function fit was confirmed using simulated CMAP scans with different motor unit number (20, 50, 100 and 150) and baseline noise (1 µV, 5 µV and 10 µV) inputs, in terms of MUNE performance, repeatability, and the test-retest reliability. For experimental data, the average MUNE of the first dorsal interosseous muscle derived from the staircase function fitting was 57.5±26.9 for the tested spinal cord injury subjects, which was significantly lower than 101.2±16.9, derived from the control group (p < 0.001). The staircase function fitting provides an appropriate approach to CMAP scan processing, yielding MUNE and other useful parameters for examination of motor unit loss and muscle fiber reinnervation.

A single center report of MScanFit motor unit number estimation in five muscles of healthy subjects.

The objective of this study was to estimate the number of motor units in 5 muscles from healthy individuals using the MScanFit program based on compound muscle action potential (CMAP) scan recordings. The examined muscles included first dorsal interosseous (FDI), abductor pollicis brevis (APB), abductor digiti minimi (ADM), second lumbrical (SL), and abductor hallucis (AH). CMAP scans were recorded from a total of 24 healthy participants. Motor unit number estimation (MUNE) values were derived from the MScanFit program. The average MUNE was 136.1 ± 31.1 (mean ± standard deviation) for the FDI, 134.9 ± 37.4 for the APB, 127.3 ± 32.3 for the ADM, 39.6 ± 8.3 for the SL, and 143.9 ± 28.9 for the AH muscles. Findings of the study provide useful information of the MScanFit MUNE for the examined muscles of healthy subjects from a single center.

Motor Unit Number Estimation of the Second Lumbrical Muscle in Human Hand.

The number of motor units of the lumbrical muscles in human hand has not been explored. The objective of this study was to fill this gap by estimating the number of motor units in the second lumbrical muscle. Compound muscle action potential scan of the second lumbrical muscle was performed in 12 healthy subjects, with 10 of them being tested on two separate occasions. Motor unit number estimation (MUNE) was derived from the MScanFit program. The average MUNE of the second lumbrical muscle was 41.6 ± 2.1 (mean ± standard error) from 12 subjects in the first test, and 42.0 ± 2.2 from 10 of the 12 subjects in the retest, demonstrating excellent measurement reliability. Findings of the study provide novel information about the motor unit number of the second lumbrical muscle in human hand. The relatively low motor unit number in the muscle can facilitate motor unit investigations, especially at high level muscle activation.