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Automatic speech recognition (ASR) based on surface electromyography (sEMG) sensors is an important technology converting electrical signals into computer-readable textual messages, which can overcome the limitation of acoustic sensors that are easily contaminated by environmental noises. However, current placements of sEMG sensors mainly depend on the experimenter’s experience, which could miss important information about the major muscular activities and lead to the decline of classification performance. In this study, 120 closely-spaced sEMG sensors were utilized to collect high-density sEMG signals for recognizing ten digits in English and Chinese. The linear discriminant analysis classifier was used to classify the speaking tasks, and the sequential forward selection algorithm was utilized for analyzing the optimal position of the sensors. The results showed that the HD sEMG energy maps could help visualize the dynamic muscle activities during the speaking process, and significantly different muscular contraction patterns were observed for different speaking tasks. The classification accuracies when using the facial sensors were significantly lower than those on the neck, although with the same number of sensors. Moreover, the classification rates could be higher than 90% with only 15 optimally selected sensors that were mainly distributed on the neck instead of the face. This study suggests that the neck muscles could be the main contributor, and more sEMG sensors should be placed on the neck to improve the ASR performance. The findings of this study could provide valuable clues for the development of a practical sEMG-based speech recognition system, especially for patients with speaking disorders.

© 2019 Anatomical Society This study aimed to investigate the spatial distribution and redistribution of lumbar erector spinae (ES) activity during a lumbar extension endurance task in pain-free participants and how this is modified in people with low back pain (LBP). High density surface electromyography (HDEMG) was recorded using 13 × 5 electrode grids placed over the lumbar ES in 13 LBP and 13 control participants while completing an Ito test to task failure. The root mean square of the HDEMG signals was computed, a topographical map of the EMG amplitude generated and the centre of the activity (centroid) determined throughout the task. The centroid of the EMG amplitude map was systematically more cranial (F = 6.09, P = 0.022) for the LBP participants compared with the control subjects. Regression analysis showed that the extent of redistribution of ES activity was associated with longer endurance. These results show that LBP participants utilised a different motor strategy to perform the endurance task, characterised by greater activation of more cranial regions of the ES and less redistribution of ES activity throughout the task. This study provides new insight into the functional activation of the lumbar ES and how it is modified when people have pain.

Facial muscle activities are essential for the appearance and communication of human beings. Therefore, exploring the activation patterns of facial muscles can help understand facial neuromuscular disorders such as Bell’s palsy. Given the irregular shape of the facial muscles as well as their different locations, it should be difficult to detect the activities of whole facial muscles with a few electrodes. In this study, a high-density surface electromyogram (HD sEMG) system with 90 electrodes was used to record EMG signals of facial muscles in both healthy and Bell’s palsy subjects when they did different facial movements. The electrodes were arranged in rectangular arrays covering the forehead and cheek regions of the face. The muscle activation patterns were shown on maps, which were constructed from the Root Mean Square (RMS) values of all the 90-channel EMG recordings. The experimental results showed that the activation patterns of facial muscles were distinct during doing different facial movements and the activated muscle regions could be clearly observed. Moreover, two features of the activation patterns, 2D correlation coefficient (corr2) and Centre of Gravity (CG) were extracted to quantify the spatial symmetry and the location of activated muscle regions respectively. Furthermore, the deviation of activated muscle regions on the paralyzed side of a face compared to the healthy side was quantified by calculating the distance between two sides of CGs. The results revealed that corr2 of the activated facial muscle region (classified into forehead region and cheek region) in Bell’s palsy subjects was significantly (p < 0.05) lower than that in healthy subjects, while CG distance of activated facial region in Bell’s palsy subjects was significantly (p < 0.05) higher than that in healthy subjects. The correlation between corr2 of these regions and Bell’s palsy [assessed by the Facial Nerve Grading Scale (FNGS) 2.0] was also significant (p < 0.05) in Bell’s palsy subjects. The spatial information on activated muscle regions may be useful in the diagnosis and treatment of Bell’s palsy in the future.

We investigated whether identification of different finger tasks only relying on the agonist or antagonist extensor digitorum communis (EDC) can be improved by using high-density sEMG (HDsEMG) pre-processed with principal component analysis (PCA). Monopolar HDsEMG was respectively recorded from EDC when the EDC muscle respectively acted as agonist or antagonist muscles. PCA-based approach was evaluated using k-nearest neighbour (KNN) classifier and compared with the classical spatial filters. Using PCA-based configuration can achieve better classification performance in identification of tasks and effort levels and dramatically outperformed spatial filtering configurations in all cases (p

In recent years, many new decomposition methods for identification of motor unit (MU) firings from high-density surface electromyograms (HDEMG) have been developed, with recent attempts focused on the use of different neural networks (NN). In this study, we evaluated the need for HDEMG signal whitening in NN-based MU identification. For this purpose, we analyzed the learning efficiency of two different types of NN, namely dense NN and long short-term memory (LSTM) NN, on the same HDEMG signals, with and without spatial whitening applied to them. All the HDEMG signals used were simulated with advanced HDEMG simulator, providing a full control of MU firing patterns and MU characteristics in our test environment. Spatial whitening of HDEMG signals significantly improved the precision of MU identification, regardless of the type of NN tested. For dense NN, precision of identified MU increased from 32.2 ± 20.2% to 93.1 ± 8.7%, whereas miss rate decreased from 48.4 ± 23.9% to 12.0 ± 13.3% when whitening of HDEMG signals was employed. For LSTM NN the precision of MU identification increased from 59.7 ± 19.7% to 99.4 ± 2.0% whereas miss rate decreased from 43.1 ± 22.3% to 12.7 ± 9.7% with whitening.

The aim of this study was to elucidate the characteristics of the motor unit (MU) firing rate in Charcot-Marie-Tooth disease type 1A (CMT1A) patients and its longitudinal change using high-density surface-electromyography (surface-EMG) and MU decomposition analysis.Nineteen patients with CMT1A and 21 force-matched healthy controls prospectively underwent surface-EMG recording of the vastus lateralis muscle during ramp-up and sustained contractions on performing isometric knee extension. After decomposition analysis, instantaneous firing rates (IFRs) of individually identified MUs were calculated. In CMT1A patients, follow-up measurements were performed one year after the baseline. Comparison of IFRs and clinical variables between CMT1A patients and controls at the baseline and between the baseline and after one year in CMT1A patients was performed.Mean IFRs of MUs were lower in CMT1A patients than in controls. This was true at various force levels in ramp-up contractions (p 0.01. e.g., 10.3 (CMT1A patients) vs. 12.2 (controls) pulses-per-second (pps) at 22.5-27.5% of maximal voluntary contraction (MVC) in MUs recruited at 7.5% of MVC) and at any time-point during sustained contractions (p 0.001. e.g., 8.0 vs. 9.3 pps, respectively, at 10-20 seconds). In CMT1A patients, mean IFRs at 0-10 seconds of sustained contraction were significantly decreased over one year (from 8.06 to 7.52 pps; p = 0.027), whereas the disease severity score and MVC of knee extension did not change over time.CMT1A patients had a lower individual MU firing rate.The MU firing rate is a potential short-term biomarker of axonal damage in CMT1A patients.

Objective. Silent speech recognition (SSR) based on surface electromyography (sEMG) is an attractive non-acoustic modality of human-machine interfaces that convert the neuromuscular electrophysiological signals into computer-readable textual messages. The speaking process involves complex neuromuscular activities spanning a large area over the facial and neck muscles, thus the locations of the sEMG electrodes considerably affected the performance of the SSR system. However, most of the previous studies used only a quite limited number of electrodes that were placed empirically without prior quantitative analysis, resulting in uncertainty and unreliability of the SSR outcomes. Approach. In this study, the technique of high-density sEMG was proposed to provide a full representation of the articulatory muscle activities so that the optimal electrode configuration for SSR could be systemically explored. A total of 120 closely spaced electrodes were placed on the facial and neck muscles to collect the high-density sEMG signals for classifying ten digits (0–9) silently spoken in both English and Chinese. The sequential forward selection algorithm was adopted to explore the optimal electrodes configurations. Main Results. The results showed that the classification accuracy increased rapidly and became saturated quickly when the number of selected electrodes increased from 1 to 120. Using only ten optimal electrodes could achieve a classification accuracy of 86% for English and 94% for Chinese, whereas as many as 40 non-optimized electrodes were required to obtain comparable accuracies. Also, the optimally selected electrodes seemed to be mostly distributed on the neck instead of the facial region, and more electrodes were required for English recognition to achieve the same accuracy. Significance. The findings of this study can provide useful guidelines about electrode placement for developing a clinically feasible SSR system and implementing a promising approach of human-machine interface, especially for patients with speaking difficulties.

Up to 85% of women with interstitial cystitis/bladder pain syndrome have pelvic floor dysfunction and hypertonicity. Current evaluation methodologies lack objective measures of pelvic floor muscle activity. We examined the ability of using intravaginal high-density surface electromyography to quantitatively, objectively and noninvasively map pelvic floor muscle activity and innervation zone locations in patients with interstitial cystitis/bladder pain syndrome.Fifteen women with interstitial cystitis/bladder pain syndrome and 15 controls underwent 2 sessions of digital pelvic examinations and high-density surface electromyography assessments. The root mean squared amplitude of high-density surface electromyography was first calculated, and the resting root mean squared ratio was then calculated by normalizing the resting electromyography root mean squared to the peak electromyography amplitude reached during maximum voluntary contraction. Innervation zone distributions were obtained from decomposed high-density surface electromyography signals. The correlation between the root mean squared ratio and interstitial cystitis/bladder pain syndrome symptom scores and pelvic floor muscle alignment were investigated in patients with interstitial cystitis/bladder pain syndrome and healthy controls.Women with interstitial cystitis/bladder pain syndrome demonstrated significantly increased resting root mean squared ratios compared to controls (0.155±0.048 vs 0.099±0.041, p=0.0019). Significant correlations were found between resting root mean squared ratio and patient reported pain (rIntravaginal high-density surface electromyography offers an objective and quantitative strategy to noninvasively assess pelvic floor muscle dysfunction in women with interstitial cystitis/bladder pain syndrome. Abundant spatiotemporal muscle activity information captured by high-density surface electromyography allows for mapping innervation zone distributions for major pelvic floor muscles.

Objective. It is of great value to accurately localize innervation zones (IZs) to better diagnose and treat neuromuscular diseases, but it is challenging to do so noninvasively from surface electromyography (sEMG) recordings because of the blurring/distorting effect of the low conductive fat tissues. This study aimed to develop an innovative transcutaneous IZ imaging (TIZI) technique to precisely and efficiently localize the IZ distribution directly over the muscle surface in vivo from high-density sEMG recordings (HD-sEMG). Approach. The TIZI technique was implemented by incorporating HD-sEMG recording, signal decomposition, finite element analysis and inverse calculation. The performance of TIZI was evaluated on the flexor digitorum superficialis (FDS) muscle with simulated sEMG signal and experimental signal recorded from both healthy (n = 3) and stroke participants (n = 4). The accuracy of imaging was validated by both of the Pearson correlation coefficient (PCC) and localization error (LE) between the TIZI results and the ‘true’ IZ distribution. Main results. In the simulation study, results have shown PCCs of 99.85% ± 0.11%, 99.79% ± 0.08%, 99.63% ± 0.22% and 99.31% ± 0.54% at the depth of 10, 15, 20 and 25 mm and SNR of 25 dB. PCCs of 98.74% ± 1.78% and 97.82% ± 1.20% were respectively obtained for experimental signals acquired from the healthy and spastic FDS muscles. The TIZI provided smaller LEs of 1.4 ± 0.92 mm and 2.02 ± 1.3 mm, compared to LEs of 7.42 ± 2.29 mm and 7.8 ± 1.77 mm from skin observations in healthy and spastic FDS, respectively. Significance. Results have demonstrated the high performance of the proposed TIZI technique by transcutaneous imaging of the IZ distribution of the skeletal muscles. The performance improvement can be attributed to the elimination of the blurring/distorting effect caused by the low conductive fat and high conductive skin tissues. TIZI may provide an advanced neurological tool for the clinical treatment of neuromuscular diseases, such as guiding botulinum neurotoxin injections in spasticity management.

This pilot study presents a novel application of high density surface electromyography (EMG) decomposition in pediatric patients, with a view toward promoting its potential clinical application for examination of pediatric neuromuscular diseases. Automatic progressive FastICA peel-off (APFP) framework was used to decompose high density surface EMG signals recorded from the first dorsal interosseous and abductor pollicis brevis muscles of children with spinal muscular atrophy. The performance of the decomposition with reduced EMG recording channels was also investigated. In total 131 motor units were extracted from 15 trials of 64-channel (8 ​× ​8) surface EMG. The decomposition yield reduced to 102 motor units when 36-channel (6 ​× ​6) was used, among which 84 motor units were common ones with an average matching rate of (95.68 ​± ​5.26) %. The decomposition yield further reduced to 65 motor units when only 16-channel (4 ​× ​4) was used, among which 57 were common ones with an average matching rate of (95.99 ​± ​4.56) %. These results indicate that using the APFP framework single motor unit activity can be reliably and automatically extracted from pediatric surface EMG signals recorded by an electrode array, which can facilitate potential clinical application of surface EMG as an alternative or supplement to invasive needle EMG for examination of neuromuscular diseases in children.

Key points Classic motor unit (MU) recording and analysis methods do not allow the same MUs to be tracked across different experimental sessions, and therefore, there is limited experimental evidence on the adjustments in MU properties following training or during the progression of neuromuscular disorders. We propose a new processing method to track the same MUs across experimental sessions (separated by weeks) by using high-density surface electromyography. The application of the proposed method in two experiments showed that individual MUs can be identified reliably in measurements separated by weeks and that changes in properties of the tracked MUs across experimental sessions can be identified with high sensitivity. These results indicate that the behaviour and properties of the same MUs can be monitored across multiple testing sessions. The proposed method opens new possibilities in the understanding of adjustments in motor unit properties due to training interventions or the progression of pathologies. Abstract A new method is proposed for tracking individual motor units (MUs) across multiple experimental sessions on different days. The technique is based on a novel decomposition approach for high-density surface electromyography and was tested with two experimental studies for reliability and sensitivity. Experiment I (reliability): ten participants performed isometric knee extensions at 10, 30, 50 and 70% of their maximum voluntary contraction (MVC) force in three sessions, each separated by 1 week. Experiment II (sensitivity): seven participants performed 2 weeks of endurance training (cycling) and were tested pre–post intervention during isometric knee extensions at 10 and 30% MVC. The reliability (Experiment I) and sensitivity (Experiment II) of the measured MU properties were compared for the MUs tracked across sessions, with respect to all MUs identified in each session. In Experiment I, on average 38.3% and 40.1% of the identified MUs could be tracked across two sessions (1 and 2 weeks apart), for the vastus medialis and vastus lateralis, respectively. Moreover, the properties of the tracked MUs were more reliable across sessions than those of the full set of identified MUs (intra-class correlation coefficients ranged between 0.63—0.99 and 0.39–0.95, respectively). In Experiment II, ∼40% of the MUs could be tracked before and after the training intervention and training-induced changes in MU conduction velocity had an effect size of 2.1 (tracked MUs) and 1.5 (group of all identified motor units). These results show the possibility of monitoring MU properties longitudinally to document the effect of interventions or the progression of neuromuscular disorders.

Introduction: The objective of this study was to determine whether motor evoked potentials (MEPs) elicited with transcranial magnetic stimulation and measured with conventional bipolar electromyography (EMG) are influenced by crosstalk from non-target muscles. Methods: MEPs were recorded in healthy participants using conventional EMG electrodes placed over the extensor carpi radialis muscle (ECR) and high-density surface EMG (HDsEMG). Fifty MEPs at 120% resting and active motor threshold were recorded. To determine the contribution of ECR to the MEPs, the amplitude distribution across HDsEMG channels was correlated with EMG activity recorded during a wrist extension task. Results: While the conventional EMG identified MEPs from ECR in >90% of the stimulations, HDsEMG revealed that spatial amplitude distribution representative of ECR activation was observed less frequently at rest than while holding a contraction (P

Speaking activities requires coordinated neuromuscular activation of the facial and neck muscles, so that natural and intelligible speeches could be produced for human communication. Given that any problem of these muscles will lead to speaking difficulties, understanding the contraction patterns of the facial and neck muscles is helpful to explore the muscular mechanism of various speaking problems. In this study, the high-density surface electromyography (HD sEMG) technique was proposed to examine the muscular activities associated with speaking. The HD sEMG signals were acquired when the human subjects were speaking English daily words by 120 channels of closely-spaced electrodes, symmetrically placed on the left and right sides of the facial/neck muscles. The results showed that the energy maps calculated from normalized RMS values of the sEMG signals could illustrate the dynamic spatiotemporal properties of the muscle activities during different speaking tasks. There were high left-right symmetric properties for the RMS curves and energy maps, and further analyses of the correlation coefficients confirmed a significant left-right correlation for the facial and neck muscles during the speaking. The findings of this study suggested that the HD sEMG signals would be useful to evaluate the muscle contraction patterns related to speaking activities and could be a potential tool for diagnosing the muscular functions of speaking difficulties.

In this study, we proposed a multi-muscle contraction force estimation framework and implemented it on the elbow flexion task to explore the contributions of different muscles to integrated force at different force levels. High density surface electromyography (HD-sEMG) signals were collected from four muscle groups and the wrist pulling force was measured synchronously. The deep belief network (DBN) was adopted to establish the relationship between HD-sEMG and force. The representative signals of four primary areas, which were considered as the input signal of the force estimation model, were extracted from HD-sEMG by principle component analysis (PCA) algorithm, then fed separately or in common to the DBN to estimate the generated force. And the contributions of different muscle groups to the generated force was analyzed with an index called mean impact value (MIV). The experimental result demonstrates that in multi-muscle contraction task, not all muscles are suitable for force estimation, the force estimation accuracy obtained using only one muscle approximates even exceeds that obtained using multiple muscles, and the relative contributions of different muscle groups to the force can be obtained according to the ranking of MIVs. This scheme provides an effective method for muscle force estimation in multi-muscle contraction tasks, and can be further applied to biomechanics, sports and rehabilitation medicine.

Trunk-movement involves coordinated activity of different lumbar muscles. By assessing the lumbar muscles activity, the pathogeny of some neuromuscular disease might be revealed. Surface electromyography (sEMG) could be used to measure the muscle activity, but for assessing lumbar muscles coordinated activity, there lacks of an accurate and comprehensive application of sEMG. High-density (HD) sEMG provides a potential to assess lumbar muscles coordinated activity more accurately. Thus, in this pilot study, the objective was to assess the lumbar muscles coordinated activity based on HD sEMG. By placing a 5×15 array (75 channels) of HD sEMG electrodes to the surface of the low back area, the sEMG signal from four healthy subjects could be collected. In order to analyze the lumbar muscles coordinated activity, the sEMG signal during different trunk-movements was recorded. Through calculating the root-mean-square (RMS) of each channel and interpolating the RMS value between channels, the sEMG topography could be obtained. The high activity area in the topography showed a regular distribution during different trunk-movements. It might be useful for further assessment of lumbar disease such as low back pain.

Despite the high prevalence of low back pain (LBP) in rowers, there are few studies investigating changes in lumbar muscle activation in rowers with a recent history of LBP. Such knowledge is relevant to understand potential mechanisms contributing to the maintenance and recurrence of LBP in rowers. For the first time, we evaluate the spatial distribution of erector spinae (ES) activity in rowers with and without a recent history of LBP, using a novel application of high-density surface electromyography (HDEMG).Cross-sectional study.Asymptomatic rowers (N=10) and rowers with a recent history of LBP (N=8) performed 7×4-min exercise bouts (rowing ergometer) until volitional exhaustion. HDEMG signals were acquired bilaterally over the lumbar ES and the root mean square (RMS) amplitude and entropy were analyzed. In addition, the y-axis coordinate of the barycentre (RMS-map) was used to assess changes in ES spatial activation.As the load increased, rowers with LBP showed higher amplitude (p0.01) and less complexity (entropy) of the HDEMG signals (p0.001). In addition, rowers with LBP showed opposite displacements of the barycentre, specifically showing a caudal shift of muscle activity at high intensities (p0.001).Both the magnitude of activation and distribution of ES activity were altered in rowers with a recent history of LBP. The lower complexity of signals together with the caudal displacements of the barycentre suggest an inefficient recruitment of the ES as the load progressed. Modification of the rowing technique in conjunction with feedback from HDEMG might prove useful in future studies.

Phonation is generated by sequentially closing and opening of vocal folds, which is controlled by the laryngeal musculature. Pitch that is the highest contributor to generate natural voice depends on the contraction of muscles over the neck. Proper evaluation of muscular activities from neck muscles would be helpful to understand the phonation functions. While surface electromyography (sEMG) technique has been chosen to investigate physiological characteristics of neck muscles for pitch estimation in previous studies, it remains unclear what are the contracting patterns on both left and right sides of the neck muscles. In this study, 80 channels of high-density (HD) sEMG electrodes were recorded from three healthy volunteers when they phonated vowel (/a/) during varying pitch from normal to high. The root mean square (RMS) of the HD sEMG signals was computed within a series of analysis windows. An averaged RMS was used to construct HD sEMG energy maps for offering average energy distribution of the neck muscles. Normalized RMS (NRMS) was calculated to quantitatively display the correlation of the neck muscles between the left and right sides. The results of sEMG waveforms and NRMS values showed obvious symmetry between the left and right sides of the front neck muscles. Additionally, the NRMS across all five rows revealed a left-right significant correlation (r=0.9432 ± 0.018, p

OBJECTIVES/HYPOTHESIS Laryngeal muscle activation is a complex and dynamic process. Current evaluation methods include needle and surface electromyography (sEMG). Limitations of needle electromyography include patient discomfort, interpretive complexity, and limited duration of recording. sEMG demonstrates interpretive challenges given loss of spatial selectivity. Application of high-density sEMG (HD sEMG) arrays were evaluated for potential to compensate for spatial selectivity loss while retaining benefits of noninvasive monitoring. STUDY DESIGN Basic science. METHODS Ten adults performed phonatory tasks while a 20-channel array recorded spatiotemporal data of the anterior neck. Data were processed to provide average spectral power of each electrode. Comparison was made between rest, low-, and high-pitch phonation. Two-dimensional (2D) spectral energy maps were created to evaluate use in gross identification of muscle location. RESULTS Three phonatory tasks yielded spectral power measures across the HD sEMG array. Each electrode within the array demonstrated unique power values across all subjects (P < .001). Comparison of each electrode to itself across phonatory tasks yielded differences in all subjects during rest versus low versus high, rest versus low, and rest versus high and in 9/10 subjects (P < .001) for low versus high phonation. Symmetry of HD sEMG signal was noted. Review of 2D coronal energy maps allowed for gross identification of cricothyroid muscle amidst anterior strap musculature. CONCLUSIONS HD sEMG can be used to identify differences in anterior neck muscle activity between rest, low-, and high-pitch phonation. HD sEMG of the anterior neck holds potential to enhance diagnostic and therapeutic monitoring for pathologies of laryngeal function. LEVEL OF EVIDENCE NA Laryngoscope, 129:2347-2353, 2019.

Cortex-muscle coherence (CMC) reflects coupling between magnetoencephalography (MEG) and surface electromyography (sEMG), being strongest during isometric contraction but absent, for unknown reasons, in some individuals. We used a novel nonmagnetic high-density sEMG (HD-sEMG) electrode grid (36 mm × 12 mm; 60 electrodes separated by 3 mm) to study effects of sEMG recording site, electrode derivation, and rectification on the strength of CMC. Monopolar sEMG from right thenar and 306-channel whole-scalp MEG were recorded from 14 subjects during 4-min isometric thumb abduction. CMC was computed for 60 monopolar, 55 bipolar, and 32 Laplacian HD-sEMG derivations, and two derivations were computed to mimic “macroscopic” monopolar and bipolar sEMG (electrode diameter 9 mm; interelectrode distance 21 mm). With unrectified sEMG, 12 subjects showed statistically significant CMC in 91–95% of the HD-sEMG channels, with maximum coherence at ∼25 Hz. CMC was about a fifth stronger for monopolar than bipolar and Laplacian derivations. Monopolar derivations resulted in most uniform CMC distributions across the thenar and in tightest cortical source clusters in the left rolandic hand area. CMC was 19–27% stronger for HD-sEMG than for “macroscopic” monopolar or bipolar derivations. EMG rectification reduced the CMC peak by a quarter, resulted in a more uniformly distributed CMC across the thenar, and provided more tightly clustered cortical sources than unrectifed sEMGs. Moreover, it revealed CMC at ∼12 Hz. We conclude that HD-sEMG, especially with monopolar derivation, can facilitate detection of CMC and that individual muscle anatomy cannot explain the high interindividual CMC variability.

INTRODUCTION We investigated the effect of workload and the use of pedal straps on the spatial distribution of neuromuscular activation within the rectus femoris (RF) muscle during pedaling movements. METHODS Eleven healthy men performed submaximal pedaling exercises on an electrically braked ergometer at different workloads and with or without pedal straps. During these tasks, surface electromyograms (SEMGs) were recorded from the RF using 36 electrode pairs, and central locus activation (CLA) was calculated along the longitudinal line of the muscle. RESULTS CLA moved markedly, indicating changes in spatial distribution of SEMG within the muscle, during a crank cycle under all conditions (P

In this study, we propose a method to estimate the magnitude and direction of the forces exerted by a fingertip by measuring the muscle activity in the forearm using surface electromyography (sEMG). This approach will enable therapists to provide highly efficient rehabilitation programs to patients by tailoring exercises to the patient' s motor recovery status. However, estimating the magnitude and direction of the force exerted by a fingertip is difficult because deep muscles in the forearm, such as the deep flexor muscles, play an important role in the adjustment of the fingertip force, but the activity of deep muscles is difficult to measure because of the crosstalk between muscles. In this study, the force exerted by the fingertips is estimated from the activities of the forearm muscles measured by high-density sEMG which is modified by signal processing steps. To implement the estimation process, high-density sEMG sensors were placed on the right forearms of healthy participant and the activities of their forearm muscles were recorded. The index finger and the middle finger were fixed to a force sensor and the isometric forces of these fingers were simultaneously measured as the subjects sequentially applied forces in the palmar, distal, and dorsal directions. Then, it was demonstrated that the fingertip forces can be estimated by an artificial neural network based on the measured myoelectric potentials with applied signal processing.

Background: Swallowing is a continuous process with substantive interdependencies among different muscles, and it plays a significant role in our daily life. The aim of this study was to propose a novel technique based on high-density surface electromyography (HD sEMG) for the evaluation of normal swallowing functions. Methods: A total of 96 electrodes were placed on the front neck to acquire myoelectric signals from 12 healthy subjects while they were performing different swallowing tasks. HD sEMG energy maps were constructed based on the root mean square values to visualize muscular activities during swallowing. The effects of different volumes, viscosities, and head postures on the normal swallowing process were systemically investigated by using the energy maps. Results: The results showed that the HD sEMG energy maps could provide detailed spatial and temporal properties of the muscle electrical activity, and visualize the muscle contractions that closely related to the swallowing function. The energy maps also showed that the swallowing time and effort was also explicitly affected by the volume and viscosity of the bolus. The concentration of the muscular activities shifted to the opposite side when the subjects turned their head to either side. Conclusions: The proposed method could provide an alternative method to physiologically evaluate the dynamic characteristics of normal swallowing and had the advantage of providing a full picture of how different muscle activities cooperate in time and location. The findings from this study suggested that the HD sEMG technique might be a useful tool for fast screening and objective assessment of swallowing disorders or dysphagia.

Partial or complete loss of the upper limb motor function has great impact on the activities of daily life (ADL) of post-stroke survivors. To improve the rehabilitation effect of fine motor function of forearms, a couple of recent studies focused on methods that try to decode the limb motion intent of patients through physical exercises. However, there exist a few studies on real-time active rehabilitation method for the classification of multiple hand movements. In the current investigate, a pattern-recognition based rehabilitation environment was set up using high-density surface electromyogram (HD-sEMG) and the real-time classification performance of 21 forearm motions was investigated with eight healthy subjects. The results showed that the average motion completion rate across all subjects was 91.17% + 2.86%, which suggests the potential of intention-initiated approach in assistive rehabilitation technique.

Many studies have addressed corticomuscular coherence (CMC), but broad applications are limited by low coherence values and the variability across subjects and recordings. Here, we investigated how the use of high-density surface electromyography (HDsEMG) can improve the detection of CMC. Sixteen healthy subjects performed isometric contractions at six low-force levels using a pinch-grip, while HDsEMG of the adductor pollicis transversus and flexor and abductor pollicis brevis and whole-head magnetoencephalography were recorded. Different configurations were constructed from the HDsEMG grid, such as a bipolar and Laplacian montage, as well as a montage based on principal component analysis (PCA). CMC was estimated for each configuration, and the strength of coherence was compared across configurations. As expected, performance of the precision-grip task resulted in significant CMC in the ß-frequency band (16-26 Hz). Compared with a bipolar EMG montage, all multichannel configurations obtained from the HDsEMG grid revealed a significant increase in CMC. The configuration, based on PCA, showed the largest (37%) increase. HDsEMG did not reduce the between-subject variability; rather, many configurations showed an increased coefficient of variation. Increased CMC presumably reflects the ability of HDsEMG to counteract inherent EMG signal factors-such as amplitude cancellation-which impact the detection of oscillatory inputs. In contrast, the between-subject variability of CMC most likely has a cortical origin. Copyright © 2014 the American Physiological Society.

We investigate whether visual feedback on the spatial distribution of upper trapezius muscle activity can prolong time to task failure of sustained shoulder abduction. Surface electromyographic signals were acquired with a 13x5 grid of high-density electromyography (HDEMG) electrodes from the right upper trapezius muscle of 12 healthy volunteers as they performed sustained isometric shoulder abduction at 20% of their maximum voluntary contraction torque (MVC) until volitional exhaustion. Data were collected in two sessions; one with HDEMG visual feedback on the spatial distribution of upper trapezius activity and one without feedback. Although the HDEMG amplitude maps could be voluntarily modified by the participants during the feedback condition (significant shift in the barycenter of activity towards the cranial direction, P = 0.038), this did not influence endurance time (total endurance time with HDEMG feedback: 149.01 ± 77.07 s, no feedback 141.74 ± 60.93 s, P = 0.532). Future studies should assess whether endurance performance can be enhanced by allowing changes in arm position during the task (changing fiber tension-length relationships), by providing a more individual motor strategy, and/or by manipulating the colours used for the HDEMG maps (lighter colours for higher contraction intensities).

Introduction Due to their complex anatomy, the general activation of lumbar muscles can be estimated with surface electromyography (sEMG) while intramuscular EMG is recommended to isolate information from different back muscles. Whether high-density sEMG recordings can represent the contribution of individual lumbar muscles, as identified with indwelling EMG, needs to be investigated. Material and methods Muscle activity from the right longissimus (L1 and L4) and deep and superficial multifidus (L4) was recorded in eleven participants using fine-wire electrodes during isometric trunk flexions at 20-degrees. We positioned a 13 × 5 electrodes grid over the right lumbar region (L1 to L4) to record sEMG. We used cross-correlations between non-rectified signals from indwelling and sEMG. The spatial location was determined as the medio-lateral and cranio-caudal coordinates of the centroid of the electrodes exhibiting the highest cross-correlation values. To establish an automated approach to identify the spatial distribution of sEMG recordings, we used the independent component analysis (ICA). Results On the sEMG, the centroid of multifidus activity identified with indwelling EMG was medial to that representing longissimus (P Conclusion The spatial distribution of sEMG amplitude provides information on the activation of individual lumbar muscles. Automated EMG decomposition approaches are useful to identify the distinct contribution of individual lumbar muscles using sEMG.

The limitations of conventional surface electromyography (sEMG) cause it to be unsuitable for use with the deep and compact muscles of the forearm. However, while source separation and localization techniques have been extensively explored to identify active sources in the brain using electroencephalography (EEG) signals, these techniques have not been adapted for identifying active sources in muscles using sEMG signals, despite being of a similar premise. Here, we perform an experiment to explore the capabilities of conventional EEG single-dipole localization techniques to localize the extensor digitorum and extensor indicis when selectively activated. The localization methodology consists of separating the raw sEMG signals using independent component analysis (ICA), estimating a physics-based forward model, and then correlating the obtained lead-field matrix with the ICA mixing matrix. The results show that single-dipole localization is not suitable for describing the active sources of muscles.

We investigated the possibility to identify motor units (MUs) with high-density surface electromyography (HDEMG) over experimental sessions in different days. 10 subjects performed submaximal knee extensions across three sessions in three days separated by one week, while EMG was recorded from the vastus medialis muscle with high-density electrode grids. The shapes of the MU action potentials (MUAPs) over multiple channels extracted from HDEMG decomposition were matched across sessions by cross-correlation. Forty and twenty percent of the MUs decomposed could be tracked across two and three sessions, respectively (average cross correlation 0.85 ± 0.04). The estimated properties of the matched motor units were similar across the sessions. For example, mean discharge rate and recruitment thresholds were measured with an intra-class correlation coefficient (ICCs) >0.80. These results strongly suggest that the same MUs were indeed identified across sessions. This possibility will allow monitoring changes in MU properties following interventions or during the progression of neuromuscular disorders.

The purpose of this work is to evaluate the effect of the electrode size on recorded High Density surface electromyography (HDsEMG) maps. We recorded the sEMG signals using a grid of electrodes (16 × 8) placed on biceps brachii (5 mm inter electrode distance, IED). Each instantaneous map was interpolated using the 2D Sinc function to obtain a “continuous” map (CM, with 10,000 samp/m). To simulate acquisition with different electrode sizes, each CM was filtered with a circular averaging filter (2–10 mm diameter) and re-sampled with 10 mm IED. Inverse transfer function to compensate the effect of electrode size was applied on each map. The results suggest that greater electrode size implies higher error of power (RMS2) of each map values with respect to the CM. The error of power estimation introduced by electrode size is smaller than the error due to IED.

As sport becomes more competitive, the importance of physical training assessment increases. This paper proposes a new method based on fuzzy analysis techniques for comparing images of muscle activity attained by employing high-density surface electromyography (sEMG). Along with other evaluation techniques, the proposed method could be useful to monitor the evolution of sportsmen's physical training. High-density sEMG maps were generated from sEMG signals acquired from the vastus medialis muscle of the right leg of healthy subjects. The fuzzy logic assessment method has two input variables: the muscle activation area and the mean intensity of muscle activity. Results are displayed on a graphical interface and the method was implemented as a modular, distributed application. The obtained results highlight important differences among sEMG images without pointing out the underlying physiological mechanisms. Future investigations regarding the applicability of the proposed method for long training cycles could provide crucial information about the changes in muscle activity that occur with physical exercise.

Objective To advance the motor unit number estimation (MUNE) technique using high density surface electromyography (EMG) decomposition. Methods The K-means clustering convolution kernel compensation algorithm was employed to detect the single motor unit potentials (SMUPs) from high-density surface EMG recordings of the biceps brachii muscles in eight healthy subjects. Contraction forces were controlled at 10%, 20% and 30% of the maximal voluntary contraction (MVC). Achieved MUNE results and the representativeness of the SMUP pools were evaluated using a high-density weighted-average method. Results Mean numbers of motor units were estimated as 288 ± 132, 155 ± 87, 107 ± 99 and 132 ± 61 by using the developed new MUNE at 10%, 20%, 30% and 10–30% MVCs, respectively. Over 20 SMUPs were obtained at each contraction level, and the mean residual variances were lower than 10%. Conclusions The new MUNE method allows a convenient and non-invasive collection of a large size of SMUP pool with great representativeness. It provides a useful tool for estimating the motor unit number of proximal muscles. Significance The present new MUNE method successfully avoids the use of intramuscular electrodes or multiple electrical stimuli which is required in currently available MUNE techniques; as such the new MUNE method can minimize patient discomfort for MUNE tests.

Although the behaviour of individual motor units is classically studied with intramuscular EMG, recently developed techniques allow its analysis also from EMG recorded in multiple locations over the skin surface (high-density surface EMG). The analysis of motor units from the surface EMG is useful when the insertion of needles is not desirable or not possible. Moreover, surface EMG allows the measure of motor unit properties which are difficult to assess with invasive technology (e.g., muscle fiber conduction velocity or location of innervation zones) and may increase the number of detectable motor units with respect to selective intramuscular recordings. Although some limitations remain, both the discharge pattern and muscle fiber properties of individual motor units can currently be analyzed non-invasively. This review presents the conditions and methodologies which allow the investigation of motor units with surface EMG.

The reliability of triceps surae electromyographic responses to standing perturbations in people after stroke and healthy controls is unknown. High-Density surface Electromyography (HDsEMG) is a technique that records electromyographic signals from different locations over a muscle, overcoming limitations of traditional surface EMG such as between-day differences in electrode placement. In this study, HDsEMG was used to measure responses from soleus (SOL, 18 channels) and medial and lateral gastrocnemius (MG and LG, 16 channels each) in 10 people after stroke and 10 controls. Timing and amplitude of the response were estimated for each channel of the grids. Intraclass Correlation Coefficient (ICC) and normalized Standard Error of Measurement (SEM%) were calculated for each channel individually (single-channel configuration) and on the median of each grid (all-channels configuration). Both timing (single-channel: ICC=0.75-0.96, SEM%=5.0-9.1; all-channels: ICC=0.85-0.97; SEM%=3.5-6.2%) and amplitude (single-channel: ICC=0.60-0.91, SEM%=25.1-46.6; ICC=0.73-0.95, SEM%=19.3-42.1) showed good-to-excellent reliability. HDsEMG provides reliable estimates of EMG responses to perturbations both in individuals after stroke and in healthy controls; reliability was marginally better for the all-channels compared to the single-channel configuration.

Improving our understanding of mechanisms driving thumb rotation could potentially advance the design of current upper limb prosthesis to incorporate powered thumb control increasing the dexterity of the device. This study investigates thumb rotation using high density surface electromyography (HD-sEMG) of extrinsic hand muscles. An apparatus was designed and developed to test thumb rotation. Relevant data were acquired during isometric voluntary contractions with a 20-40% of maximum voluntary contraction (MVC) performed by 7 healthy right-handed volunteers. Data were processed and analyzed. Analysis showed that, thumb position could be extracted using features extracted from the HD-sEMG data recorded with high statistical significance.

High Density surface Electromyography (HDsEMG) has been applied in both research and clinical applications for non-invasive neuromuscular assessment in several different fields using 2-D array. Proper interpretation of HDsEMG signals requires identifying “good” channels (where there is no short-circuit or bad-contact or major power line interference problem). Recording with many channels usually implies bad-contacts (that introduces large power line interference) and short-circuits (when using gels). In addition to online monitoring the electrode-contact quality, it is necessary to identify “bad” channels, or outliers, prior to the analysis of HDsEMG signal. In this paper we introduce a robust method to identify outliers in a set of monopolar HDsEMG signals recorded from Biceps and Triceps Brachii,Anconeus, Brachioradialis and Pronator Teres. The sensitivity and precision of this method show that this approach is promising.