Spatial localization of electromyographic amplitude distributions associated to the activation of dorsal forearm muscles

Myoelectric manifestations of fatigue of the finger flexor muscles and endurance capacity in experienced versus intermediate climbers during suspension tasks

Climbing is a physically demanding discipline, placing significant loads on the finger flexors. Notwithstanding the documented greater endurance capacity of experienced climbers, the mechanisms explaining these training-induced adaptations remain unknown. We therefore investigate whether two non-competing strategies - muscle adaptation and alternate muscle recruitment - may explain the disparity in endurance capacity in participants with different climbing experience. We analysed high-density surface electromyograms (EMGs) from 38 Advanced and Intermediate climbers, during suspension exercises over three different depths (15, 20, 30 mm) using a half-crimp grip position. From the spatial distribution of changes in MeDian Frequency and Root Mean Square values until failure, we assessed how much and how diffusely the myoelectric manifestations of fatigue took place. Advanced climbers exhibited greater endurance, as evidenced by significantly longer failure time (p < 0.009) and lower changes in MDF values (p < 0.013) for the three grip depths. These changes were confined to a small skin region (nearly 25% of the grid size), centred at variable locations across participants. Moreover, lower MDF changes were significantly associated with longer suspension times. Collectively, our results suggest that muscle adaptation rather than load sharing between and within muscles is more likely to explain the improved endurance in experienced climbers.

Simulating progressive motor neuron degeneration and collateral reinnervation in motor neuron diseases using a dynamic muscle model based on human single motor unit recordings

Objective.To simulate progressive motor neuron loss and collateral reinnervation in motor neuron diseases (MNDs) by developing a dynamic muscle model based on human single motor unit (MU) surface-electromyography (EMG) recordings.Approach.Single MU potentials recorded with high-density surface-EMG from thenar muscles formed the basic building blocks of the model. From the baseline MU pool innervating a muscle, progressive MU loss was simulated by removal of MUs, one-by-one. These removed MUs underwent collateral reinnervation with scenarios varying from 0% to 100%. These scenarios were based on a geometric variable, reflecting the overlap in MU territories using the spatiotemporal profiles of single MUs and a variable reflecting the efficacy of the reinnervation process. For validation, we tailored the model to generate compound muscle action potential (CMAP) scans, which is a promising surface-EMG method for monitoring MND patients. Selected scenarios for reinnervation that matched observed MU enlargements were used to validate the model by comparing markers (including the maximum CMAP and a motor unit number estimate (MUNE)) derived from simulated and recorded CMAP scans in a cohort of 49 MND patients and 22 age-matched healthy controls.Main results.The maximum CMAP at baseline was 8.3 mV (5th-95th percentile: 4.6 mV-11.8 mV). Phase cancellation caused an amplitude drop of 38.9% (5th-95th percentile, 33.0%-45.7%). To match observations, the geometric variable had to be set at 40% and the efficacy variable at 60%-70%. The Δ maximum CMAP between recorded and simulated CMAP scans as a function of fitted MUNE was -0.4 mV (5th-95th percentile = -4.0 - +2.4 mV).Significance.The dynamic muscle model could be used as a platform to train personnel in applying surface-EMG methods prior to their use in clinical care and trials. Moreover, the model may pave the way to compare biomarkers more efficiently, without directly posing unnecessary burden on patients.

Tutorial. Surface electromyogram (sEMG) amplitude estimation: Best practices

This tutorial intends to provide insight, instructions and "best practices" for those who are novices-including clinicians, engineers and non-engineers-in extracting electromyogram (EMG) amplitude from the bipolar surface EMG (sEMG) signal of voluntary contractions. A brief discussion of sEMG amplitude extraction from high density sEMG (HDsEMG) arrays and feature extraction from electrically elicited contractions is also provided. This tutorial attempts to present its main concepts in a straightforward manner that is accessible to novices in the field not possessing a wide range of technical background (if any) in this area. Surface EMG amplitude, also referred to as the sEMG envelope [often implemented as root mean square (RMS) sEMG or average rectified value (ARV) sEMG], quantifies the voltage variation of the sEMG signal and is grossly related to the overall neural excitation of the muscle and to peripheral parameters. The tutorial briefly reviews the physiological origin of the voluntary sEMG signal and sEMG recording, including electrode configurations, sEMG signal transduction, electronic conditioning and conversion by an analog-to-digital converter. These topics have been covered in greater detail in prior tutorials in this series. In depth descriptions of state-of-the-art methods for computing sEMG amplitude are then provided, including guidance on signal pre-conditioning, absolute value vs. square-law detection, selection of appropriate sEMG amplitude smoothing filters and attenuation of measurement noise. The tutorial provides a detailed list of best practices for sEMG amplitude estimation.

Effect of postural differences on the activation of intrinsic foot muscles during ramp-up toe flexion in young men

BACKGROUND

Intrinsic foot muscle exercises are used in clinical and sports practice to improve performance. Force generation during toe flexion is greater in the standing posture than in the sitting posture; nonetheless, the mechanism underlying the activation of intrinsic foot muscles during force generation and whether there exists a difference between these two postures still remain unclear.

RESEARCH QUESTION

Are the activities of intrinsic foot muscles affected by standing and sitting postures during gradual force generation?

METHODS

Seventeen men participated in the laboratory based cross-sectional study. Each participant performed a force ramp-up toe flexion task from 0% to 80% of the maximal toe flexor strength (MTFS) in sitting and standing postures. High-density surface electromyography signals obtained during the task were determined by calculating the root mean square (RMS). Additionally, modified entropy and coefficient of variation (CoV) were calculated at 20-80 % MTFS for each 10 % MTFS.

RESULTS

The RMS between the two postures indicated an interaction effect (p < 0.01). Post-hoc analyses revealed that intrinsic foot muscle activity during the ramp-up task was significantly higher in the standing posture than in the sitting posture at 60 % MTFS (67.53 ± 15.91 vs 54.64 ± 19.28 % maximal voluntary contraction [MVC], p = 0.03), 70 % MTFS (78.11 ± 12.93 vs 63.28 ± 18.65 % MVC, p = 0.01), and 80 % MTFS (81.78 ± 14.07 vs 66.90 ± 20.32 % MVC, p = 0.02). In the standing posture, the modified entropy at 80 % MTFS was lower than that at 20 % MTFS (p = 0.03), and the CoV at 80 % MTFS was higher than that at 20 % MTFS (p = 0.03).

SIGNIFICANCE

These results indicated that posture selection is important for high-intensity exercises of the intrinsic foot muscles, such as resistance training. Thus, improving performance related to toe flexor strength might be more effective when conducted under adequate weight-bearing situations, such as in the standing posture.

Alterations in forearm muscle activation patterns after scapholunate interosseous ligament injury: A dynamic electromyography study

STUDY DESIGN

Case control.

PURPOSE OF THE STUDY

This study aimed to investigate the alterations seen in the activation patterns of the forearm muscles and to demonstrate the associated functional outcomes, in patients with scapholunate interosseous ligament (SLIL) injury.

METHODS

The study involved 15 patients with SLIL injury (instability group) and 11 healthy participants (control group). Both groups were evaluated with regard to their pain, grip strength, and upper extremity functional level (disabilities of the arm, shoulder and hand and patient-rated wrist evaluation questionnaires), and they also underwent a dynamic electromyography analysis of their forearm muscle activity. The activation patterns of the extensor carpi ulnaris (ECU), extensor carpi radialis (ECR), flexor carpi ulnaris, and flexor carpi radialis muscles during wrist extension and flexion were recorded by means of surface electromyography.

RESULTS

In the instability group, the pain severity was higher and the functional level was worse than in the control group (P < .05). Furthermore, during wrist extension, the ECR activity was lower and the ECU activity was higher in the instability group than in the control group (P < .05).

CONCLUSION

Dynamic stabilization of the wrist, flexor carpi ulnaris, and flexor carpi radialis muscles have been shown to play an active role with ECU and ECR. Increased ECU and decreased ECR activation may pose a potential risk in terms of enhancing the scapholunate gap. We, therefore, propose that appropriate preventive neuromuscular exercise strategies implemented as part of a physiotherapy program for patients with SLIL lesions might increase the contribution of the dynamic stability effect of the relevant muscles.

Bipolar versus high-density surface electromyography for evaluating risk in fatiguing frequency-dependent lifting activities

Workers often develop low back pain due to manually lifting heavy loads. Instrumental-based assessment tools are used to quantitatively assess the biomechanical risk in lifting activities. This study aims to verify the hypothesis that high-density surface electromyography (HDsEMG) allows an optimized discrimination of risk levels associated with different fatiguing lifting conditions compared to traditional bipolar sEMG. 15 participants performed three lifting tasks with a progressively increasing lifting index (LI) each lasting 15 min. Erector spinae (ES) activity was recorded using both bipolar and HDsEMG systems. The amplitude of both bipolar and HDsEMG can significantly discriminate each pair of LI. HDsEMG data could discriminate across the different LIs starting from the fourth minute of the task while bipolar sEMG could only do so towards the end. The higher discriminative power of HDsEMG data across the lifting tasks makes such methodology a valuable tool to be used to monitor fatigue while lifting and could extend the possibilities offered by currently available instrumental-based tools.

Contribution of the peroneus longus neuromuscular compartments to eversion and plantarflexion of the ankle

Compartmentalization of animal and human skeletal muscle by multiple motor nerve branches known as the neuromuscular compartment (NMC) has been observed primarily in muscles that participate in a plane of motion. In this context, the peroneus longus muscle contributes to eversion and plantarflexion of the ankle and the presence of NMCs has been reported. However, no research has reported the selective activation of the compartments of the peroneus longus during the performance of different ankle movements. The purpose of this research was to determine the contribution of peroneus longus NMCs, through multi-channel surface electromyography (sEMG), to eversion and plantarflexion movements. Multi-channel sEMG was recorded from the peroneus longus muscle by using an electrode grid during eversion and plantarflexion of the ankle at 10%, 30%, 50%, and 70% of maximal voluntary isometric contraction (MVIC). The root mean square and displacement of the center of mass position in the X (COMx) and Y (COMy) components were calculated. The primary finding was that eversion showed significantly higher sEMG amplitude than plantarflexion in the posterior compartment in low, moderate, and high percentages of MVIC. However, no significant difference in sEMG amplitude was observed in the anterior compartment between eversion and plantarflexion. In addition, a posterior displacement of the COMx in eversion compared to plantarflexion in all MVIC percentages, with greater topographic distancing of the COMx at higher levels of activation. In conclusion, the peroneus longus muscle presented NMCs; the anterior compartment contributed to both eversion and plantarflexion movements, whereas the posterior compartment mainly contributed to the eversion movement of the ankle in low, moderate, and high percentages of MVIC.

Exhausting repetitive piano tasks lead to local forearm manifestation of muscle fatigue and negatively affect musical parameters

Muscle fatigue is considered as a risk factor for developing playing-related muscular disorders among professional pianists and could affect musical performance. This study investigated in 50 pianists the effect of fatiguing repetitive piano sequences on the development of forearm muscle fatigue and on piano performance parameters. Results showed signs of myoelectric manifestation of fatigue in the 42-electromyographic bipolar electrodes positioned on the forearm to record finger and wrist flexor and extensor muscles, through a significant non-constant decrease of instantaneous median frequency during two repetitive Digital (right-hand 16-tones sequence) and Chord (right-hand chords sequence) excerpts, with extensor muscles showing greater signs of fatigue than flexor muscles. In addition, muscle fatigue negatively affected key velocity, a central feature of piano sound intensity, in both Digital and Chord excerpts, and note-events, a fundamental aspect of musicians' performance parameter, in the Chord excerpt only. This result highlights that muscle fatigue may alter differently pianists' musical performance according to the characteristics of the piece played.

Quantifying Spatial Activation Patterns of Motor Units in Finger Extensor Muscles

The ability to expertly control different fingers contributes to hand dexterity during object manipulation in daily life activities. The macroscopic spatial patterns of muscle activations during finger movements using global surface electromyography (sEMG) have been widely researched. However, the spatial activation patterns of microscopic motor units (MUs) under different finger movements have not been well investigated. The present work aims to quantify MU spatial activation patterns during movement of distinct fingers (index, middle, ring and little finger). Specifically, we focused on extensor muscles during extension contractions. Motor unit action potentials (MUAPs) during movement of each finger were obtained through decomposition of high-density sEMG (HD-sEMG). First, we quantified the spatial activation patterns of MUs for each finger based on 2-dimension (2-D) root-mean-square (RMS) maps of MUAP grids after spike-triggered averaging. We found that these activation patterns under different finger movements are distinct along the distal-proximal direction, but with partial overlap. Second, to further evaluate MU separability, we classified the spatial activation pattern of each individual MU under distinct finger movement and associated each MU with its corresponding finger with Regularized Uncorrelated Multilinear Discriminant Analysis (RUMLDA). A high accuracy of MU-finger classification tested on 12 subjects with a mean of 88.98% was achieved. The quantification of MU spatial activation patterns could be beneficial to studies of neural mechanisms of the hand. To the best of our knowledge, this is the first work which manages to quantify MU behaviors under different finger movements.

Comparison of Facial Muscle Activation Patterns Between Healthy and Bell's Palsy Subjects Using High-Density Surface Electromyography

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.

Increased Muscle Activity Accompanying With Decreased Complexity as Spasticity Appears: High-Density EMG-Based Case Studies on Stroke Patients

Spasticity is a major contributor to pain, disabilities and many secondary complications after stroke. Investigating the effect of spasticity on neuromuscular function in stroke patients may facilitate the development of its clinical treatment, while the underlying mechanism of spasticity still remains unclear. The aim of this study is to explore the difference in the neuromuscular response to passive stretch between healthy subjects and stroke patients with spasticity. Five healthy subjects and three stroke patients with spastic elbow flexor were recruited to complete the passive stretch at four angular velocities (10°/s, 60°/s, 120°/s, and 180°/s) performed by an isokinetic dynamometer. Meanwhile, the 64-channel electromyography (EMG) signals from biceps brachii muscle were recorded. The root mean square (RMS) and fuzzy entropy (FuzzyEn) of EMG recordings of each channel were calculated, and the relationship between the average value of RMS and FuzzyEn over 64-channel was examined. The two groups showed similar performance from results that RMS increased and FuzzyEn decreased with the increment of stretch velocity, and the RMS was negatively correlated with FuzzyEn. The difference is that stroke patients showed higher RMS and lower FuzzyEn during quick stretch than the healthy group. Furthermore, compared with the healthy group, distinct variations of spatial distribution within the spastic muscle were found in the EMG activity of stroke patients. These results suggested that a large number of motor units were recruited synchronously in the presence of spasticity, and this recruitment pattern was non-uniform in the whole muscle. Using a combination of RMS and FuzzyEn calculated from high-density EMG (HD-EMG) recordings can provide an innovative insight into the physiological mechanism underlying spasticity, and FuzzyEn could potentially be used as a new indicator for spasticity, which would be beneficial to clinical intervention and further research on spasticity.

High-density surface electromyography signals during isometric contractions of elbow muscles of healthy humans

This paper presents a dataset of high-density surface EMG signals (HD-sEMG) designed to study patterns of sEMG spatial distribution over upper limb muscles during voluntary isometric contractions. Twelve healthy subjects performed four different isometric tasks at different effort levels associated with movements of the forearm. Three 2-D electrode arrays were used for recording the myoelectric activity from five upper limb muscles: biceps brachii, triceps brachii, anconeus, brachioradialis, and pronator teres. Technical validation comprised a signals quality assessment from outlier detection algorithms based on supervised and non-supervised classification methods. About 6% of the total number of signals were identified as "bad" channels demonstrating the high quality of the recordings. In addition, spatial and intensity features of HD-sEMG maps for identification of effort type and level, have been formulated in the framework of this database, demonstrating better performance than the traditional time-domain features. The presented database can be used for pattern recognition and MUAP identification among other uses.

Inverse modelling to reduce crosstalk in high density surface electromyogram

Surface electromyogram (EMG) has a relatively large detection volume, so that it could include contributions both from the target muscle of interest and from nearby regions (i.e., crosstalk). This interference can prevent a correct interpretation of the activity of the target muscle, limiting the use of surface EMG in many fields. To counteract the problem, selective spatial filters have been proposed, but they reduce the representativeness of the data from the target muscle. A better solution would be to discard only crosstalk from the signal recorded in monopolar configuration (thus, keeping most information on the target muscle). An inverse modelling approach is here proposed to estimate the contributions of different muscles, in order to focus on the one of interest. The method is tested with simulated monopolar EMGs from superficial nearby muscles contracted at different force levels (either including or not model perturbations and noise), showing statistically significant improvements in information extraction from the data. The median over the entire dataset of the mean squared error in representing the EMG of the muscle under the detection electrode was reduced from 11.2% to 4.4% of the signal energy (5.3% if noisy data were processed); the median bias in conduction velocity estimation (from 3 monopolar channels aligned to the muscle fibres) was decreased from 2.12 to 0.72 m/s (1.1 m/s if noisy data were processed); the median absolute error in the estimation of median frequency was reduced from 1.02 to 0.67 Hz in noise free conditions and from 1.52 to 1.45 Hz considering noisy data.

Intrinsic foot muscles act to stabilise the foot when greater fluctuations in centre of pressure movement result from increased postural balance challenge

BACKGROUND

Increased postural balance challenge is associated with more fluctuations in centre of pressure movement, indicating increased interference from the postural control system. The role of intrinsic foot muscles in balance control is relatively understudied and whether such control system interference occurs at the level of these muscles is unknown.

RESEARCH QUESTION

Do fewer fluctuations in intrinsic foot muscle excitation occur in response to increased postural balance challenge?

METHODS

Surface EMGs were recorded using a grid of 13 × 5 channels from the plantar surface of the foot of 17 participants, who completed three balance tasks: bipedal stance; single leg stance and bipedal tip-toe. Centre of pressure (CoP) movement was calculated from simultaneously recorded force plate signals. Fluctuations in CoP and EMGs for each task were quantified using a sample entropy based metric, Entropy Halflife (EnHL). Longer EnHL indicates fewer signal fluctuations.

RESULTS

The shortest EMG EnHL, 9.27 ± 3.34 ms (median ± interquartile range), occurred during bipedal stance and the longest during bipedal tip-toe 15.46 ± 11.16 ms, with 18.80 ± 8.00 ms recorded for single leg stance. Differences were statistically significant between bipedal stance and both bipedal tip-toe (p < 0.001) and single leg stance (p < 0.001). CoP EnHL for both anterior-posterior and medial-lateral movements also differed significantly between tasks (p < 0.001, both cases). However, anterior-posterior CoP EnHL was longest for bipedal stance 259.84±230.22 ms and shortest for bipedal tip-toe 146.25±73.35 ms. Medial-lateral CoP EnHL was also longest during bipedal stance 215.73±187.58 ms, but shortest for single leg stance 113.48±83.01 ms.

SIGNIFICANCE

Fewer fluctuations in intrinsic foot muscle excitation occur in response to increased postural balance challenge. Fluctuations in CoP movement during balance must be predominantly driven by excitation of muscles extrinsic to the foot. Intrinsic foot muscles therefore likely play a greater role in stabilisation of the foot than balance control during the postural tasks studied.

Adaptive Spatial Filtering of High-Density EMG for Reducing the Influence of Noise and Artefacts in Myoelectric Control

Electromyography (EMG) is a source of neural information for controlling neuroprosthetic devices. To enhance the information content of conventional bipolar EMG, high-density EMG systems include tens to hundreds of closely spaced electrodes that non-invasively record the electrical activity of muscles with high spatial resolution. Despite the advantages of relying on multiple signal sources, however, variations in electrode-skin contact impedance and noise remain challenging for multichannel myocontrol systems. These spatial and temporal non-stationarities negatively impact the control accuracy and therefore substantially limit the clinical viability of high-density EMG techniques. Here, we propose an adaptive algorithm for automatic artefact/noise detection and attenuation for high-density EMG control. The method infers the presence of noise in each EMG channel by spectro-temporal measures of signal similarity. These measures are then used for establishing a scoring system based on an adaptive weighting and reinforcement formulation. The method was experimentally tested as a pre-processing step for a multi-class discrimination problem of 4-digit activation. The approach was proven to enhance the discriminative information content of high-density EMG signals, as well as to attenuate non-stationary artefacts, with improvements in accuracy and robustness of the classification.

Characterization of Forearm Muscle Activation in Duchenne Muscular Dystrophy via High-Density Electromyography: A Case Study on the Implications for Myoelectric Control

Duchenne muscular dystrophy (DMD) is a genetic disorder that results in progressive muscular degeneration. Although medical advances increased their life expectancy, DMD individuals are still highly dependent on caregivers. Hand/wrist function is central for providing independence, and robotic exoskeletons are good candidates for effectively compensating for deteriorating functionality. Robotic hand exoskeletons require the accurate decoding of motor intention typically via surface electromyography (sEMG). Traditional low-density sEMG was used in the past to explore the muscular activations of individuals with DMD; however, it cannot provide high spatial resolution. This study characterized, for the first time, the forearm high-density (HD) electromyograms of three individuals with DMD while performing seven hand/wrist-related tasks and compared them to eight healthy individuals (all data available online). We looked into the spatial distribution of HD-sEMG patterns by using principal component analysis (PCA) and also assessed the repeatability and the amplitude distributions of muscle activity. Additionally, we used a machine learning approach to assess DMD individuals' potentials for myocontrol. Our analysis showed that although participants with DMD were able to repeat similar HD-sEMG patterns across gestures (similarly to healthy participants), a fewer number of electrodes was activated during their gestures compared to the healthy participants. Additionally, participants with DMD activated their muscles close to maximal contraction level (0.63 ± 0.23), whereas healthy participants had lower normalized activations (0.26 ± 0.2). Lastly, participants with DMD showed on average fewer PCs (3), explaining 90% of the complete gesture space than the healthy (5). However, the ability of the DMD participants to produce repeatable HD-sEMG patterns was unexpectedly comparable to that of healthy participants, and the same holds true for their offline myocontrol performance, disproving our hypothesis and suggesting a clear potential for the myocontrol of wearable exoskeletons. Our findings present evidence for the first time on how DMD leads to progressive alterations in hand/wrist motor control in DMD individuals compared to healthy. The better understanding of these alterations can lead to further developments for the intuitive and robust myoelectric control of active hand exoskeletons for individuals with DMD.

An Online Spectral Information-Enhanced Approach for Artifact Detection and Fault Attentuation in Myoelectric Control

In myocontrol of neuroprosthetic devices, multichannel electromyography (EMG) can be used to decode the intended motor command, based on distributed activation patterns of stump muscles. In this regard, the high density EMG (HD-EMG) approach allows for enhancement of the spatiotemporal resolution for motor intention detection. Despite the advantages of relying on several EMG channels, the challenge of high-density electrode systems is the dynamically changing electrode-skin contact impedance, which can affect a considerable number of electrodes over the time of data acquisition. This can result in obtaining unreliable, low-quality EMG recording with a distributed artifact pattern over the grid of EMG sensors. To address this issue, we propose a novel online approach for adaptive information extraction and enhancement for automatic artifact detection and attenuation in HD-EMG-based myocontrol of prosthetic devices. The method is based on an adaptive weighting scheme that modifies the contribution of each HD-EMG channel considering the spectral information content relative to artifacts. The technique (named IE-HD-EMG) was tested as an online pre-conditioning step for a challenging multiclass classification problem of 4-finger activation, using linear discriminant analysis. It is shown that for this application, the proposed IE-HD-EMG technique led to a superior performance in finger activation recognition (79.25% accuracy, 89% sensitivity, 89.15% specificity) in comparison to the conventional HD-EMG recording under the same condition without the proposed approach (56.25% accuracy, 61.3% sensitivity, 67% specificity). Therefore, the proposed technique can have a significant potential to expand the clinical viability of HD-EMG systems.

Single finger movements in the aging hand: changes in finger independence, muscle activation patterns and tendon displacement in older adults

With aging, hand mobility and manual dexterity decline, even under healthy circumstances. To assess how aging affects finger movement control, we compared elderly and young subjects with respect to (1) finger movement independence, (2) neural control of extrinsic finger muscles and (3) finger tendon displacements during single finger flexion. In twelve healthy older (age 68-84) and nine young (age 22-29) subjects, finger kinematics were measured to assess finger movement enslaving and the range of independent finger movement. Muscle activation was assessed using a multi-channel electrode grid placed over the flexor digitorum superficialis (FDS) and the extensor digitorum (ED). FDS tendon displacements of the index, middle and ring fingers were measured using ultrasound. In older subjects compared to the younger subjects, we found: (1) increased enslaving of the middle finger during index finger flexion (young: 25.6 ± 12.4%, elderly: 47.0 ± 25.1%; p = 0.018), (2) a lower range of independent movement of the index finger (young_middle = 74.0%, elderly_middle: 45.9%; p < 0.001), (3) a more evenly distributed muscle activation pattern over the finger-specific FDS and ED muscle regions and (4) a lower slope at the beginning of the finger movement to tendon displacement relationship, presenting a distinct period with little to no tendon displacement. Our study indicates that primarily the movement independence of the index finger is affected by aging. This can partly be attributed to a muscle activation pattern that is more evenly distributed over the finger-specific FDS and ED muscle regions in the elderly.

Intra-session and Inter-day Reliability of the Myon 320 Electromyography System During Sub-maximal Contractions

Electromyography systems are widely used within the field of scientific and clinical practices. The reliability of these systems are paramount when conducting research. The reliability of Myon 320 Surface Electromyography System is yet to be determined. This study aims to determine the intra-session and inter-day reliability of the Myon 320 Surface Electromyography System. Muscle activity from fifteen participants was measured at the anterior deltoid muscle during a bilateral front raise exercise, the vastus lateralis muscle during a squat exercise and the extensor carpi radialis brevis (ECRB) muscle during an isometric handgrip task. Intra-session and inter-day reliability was calculated by intraclass correlation coefficient, standard error of measurement and coefficient of variation (CV). The normalized root mean squared (RMS) surface electromyographic signals produced good intra-session and inter-day testing intraclass correlation coefficient values (range: 0.63–0.97) together with low standard error of measurement (range: 1.49–2.32) and CV (range: 95% Confidence Interval = 0.36–12.71) measures for the dynamic-and-isometric contractions. The findings indicate that the Myon 320 Surface Electromyography System produces good to fair reliability when examining intra-session and inter-day reliability. Findings of the study provide evidence of the reliability of electromyography between trials which is essential during clinical testing.

Surface electromyography can quantify temporal and spatial patterns of activation of intrinsic human foot muscles

Intrinsic foot muscles (IFM) are a crucial component within the human foot. Investigating their functioning can help understand healthy and pathological behaviour of foot and ankle, fundamental for everyday activities. Recording muscle activation from IFM has been attempted with invasive techniques, mainly investigating single muscles. Here we present a novel methodology, to investigate the feasibility of recording physiological surface EMG (sEMG) non-invasively and quantify patterns of activation across the whole plantar region of the foot. sEMG were recorded with a 13 × 5 array from the sole of the foot (n = 25) during two-foot stance, two-foot tiptoe and anterior/posterior sways. Physiological features of sEMG were analysed. During anterior/posterior epochs within the sway task, sEMG patterns were analysed in terms of signal amplitude (intensity) and structure (Sample Entropy) distribution, by evaluating the centre of gravity (CoG) of each topographical map. Results suggest signals are physiological and not affected by loading. Both amplitude and sample entropy CoG coordinates were grouped in one region and overlapped, suggesting that the region with highest amplitude corresponds with the most predictable signal. Therefore, both spatial and temporal features of IFM activation may be recorded non-invasively, providing opportunity for more detailed investigation of IFM function in healthy and patient populations.

Proportional estimation of finger movements from high-density surface electromyography

BACKGROUND

The importance to restore the hand function following an injury/disease of the nervous system led to the development of novel rehabilitation interventions. Surface electromyography can be used to create a user-driven control of a rehabilitation robot, in which the subject needs to engage actively, by using spared voluntary activation to trigger the assistance of the robot.

METHODS

The study investigated methods for the selective estimation of individual finger movements from high-density surface electromyographic signals (HD-sEMG) with minimal interference between movements of other fingers. Regression was evaluated in online and offline control tests with nine healthy subjects (per test) using a linear discriminant analysis classifier (LDA), a common spatial patterns proportional estimator (CSP-PE), and a thresholding (THR) algorithm. In all tests, the subjects performed an isometric force tracking task guided by a moving visual marker indicating the contraction type (flexion/extension), desired activation level and the finger that should be moved. The outcome measures were mean square error (nMSE) between the reference and generated trajectories normalized to the peak-to-peak value of the reference, the classification accuracy (CA), the mean amplitude of the false activations (MAFA) and, in the offline tests only, the Pearson correlation coefficient (PCORR).

RESULTS

The offline tests demonstrated that, for the reduced number of electrodes (≤24), the CSP-PE outperformed the LDA with higher precision of proportional estimation and less crosstalk between the movement classes (e.g., 8 electrodes, median MAFA ~ 0.6 vs. 1.1 %, median nMSE ~ 4.3 vs. 5.5 %). The LDA and the CSP-PE performed similarly in the online tests (median nMSE < 3.6 %, median MAFA < 0.7 %), but the CSP-PE provided a more stable performance across the tested conditions (less improvement between different sessions). Furthermore, THR, exploiting topographical information about the single finger activity from HD-sEMG, provided in many cases a regression accuracy similar to that of the pattern recognition techniques, but the performance was not consistent across subjects and fingers.

CONCLUSIONS

The CSP-PE is a method of choice for selective individual finger control with the limited number of electrodes (<24), whereas for the higher resolution of the recording, either method (CPS-PA or LDA) can be used with a similar performance. Despite the abundance of detection points, the simple THR showed to be significantly worse compared to both pattern recognition/regression methods. Nevertheless, THR is a simple method to apply (no training), and it could still give satisfactory performance in some subjects and/or simpler scenarios (e.g., control of selected fingers). These conclusions are important for guiding future developments towards the clinical application of the methods for individual finger control in rehabilitation robotics.

Filtered Virtual Reference: A New Method for the Reduction of Power Line Interference With Minimal Distortion of Monopolar Surface EMG

GOAL

This study tests and validates a new method to remove power line interference from monopolar EMGs detected by multichannel systems: the filtered virtual reference (FVR). FVR is an adaptation of the virtual reference (VR) method, which consists in referencing signals detected by each electrode in a grid to their spatial average. Signals may however be distorted with the VR approach, in particular when the skin region where the detection system is positioned does not cover the entire muscle.

METHODS

Simulated and experimental EMGs were used to compare the performance of FVR and VR in terms of interference reduction and distortion of monopolar signals referred to a remote reference.

RESULTS

Simulated data revealed the monopolar EMG signals processed with FVR were significantly less distorted than those filtered by VR. These results were similarly observed for experimental signals. Moreover, FVR method outperformed VR in removing power line interference when it was distributed unevenly across the signals of the grid.

CONCLUSION

Key results demonstrated that FVR improves the VR method as it reduces interference while preserving the information content of monopolar signals.

SIGNIFICANCE

Although the actual distribution of motor unit action potential is represented in monopolar EMGs, collecting high quality monopolar signals is challenging. This study presents a possible solution to this issue; FVR provides undistorted monopolar signals with negligible interference and is insensitive to muscle architecture. It is therefore relevant for EMG applications benefiting from a clean monopolar detection (e.g., decomposition, control of prosthetic devices, motor unit number estimation).

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.

Spatial analysis of muscular activations in stroke survivors

We investigated the spatial patterns of electrical activity in stroke-affected muscles using the high density surface electromyogram (sEMG) grids. We acquired 128-channel sEMG signals from the impaired as well as contralateral Biceps Brachii (BB) muscles of stroke survivors and from healthy participants at various force levels from 20 to 60% of maximum voluntary contraction in an isometric non-fatiguing recording protocol. We found the spatial sEMG pattern to be consistent across force levels in healthy and stroke subjects. However, once compared across sides (left vs right in healthy and impaired vs. contralateral in stroke) we found stroke-affected sides to be significantly different in distribution pattern of sEMG from the contralateral side. The sEMG activity areas were significantly shrunk on the affected sides indicating muscle atrophy due to stroke.

Extracting extensor digitorum communis activation patterns using high-density surface electromyography

The extensor digitorum communis muscle plays an important role in hand dexterity during object manipulations. This multi-tendinous muscle is believed to be controlled through separate motoneuron pools, thereby forming different compartments that control individual digits. However, due to the complex anatomical variations across individuals and the flexibility of neural control strategies, the spatial activation patterns of the extensor digitorum communis compartments during individual finger extension have not been fully tracked under different task conditions. The objective of this study was to quantify the global spatial activation patterns of the extensor digitorum communis using high-density (7 × 9) surface electromyogram (EMG) recordings. The muscle activation map (based on the root mean square of the EMG) was constructed when subjects performed individual four finger extensions at the metacarpophalangeal joint, at different effort levels and under different finger constraints (static and dynamic). Our results revealed distinct activation patterns during individual finger extensions, especially between index and middle finger extensions, although the activation between ring and little finger extensions showed strong covariance. The activation map was relatively consistent at different muscle contraction levels and for different finger constraint conditions. We also found that distinct activation patterns were more discernible in the proximal–distal direction than in the radial–ulnar direction. The global spatial activation map utilizing surface grid EMG of the extensor digitorum communis muscle provides information for localizing individual compartments of the extensor muscle during finger extensions. This is of potential value for identifying more selective control input for assistive devices. Such information can also provide a basis for understanding hand impairment in individuals with neural disorders.