Electromyography Signals Research Articles

Hypercapnia impacts neural drive and timing of diaphragm neuromotor control.

The neuromotor control of the diaphragm muscle (DIAm) involves motor unit recruitment, sustained activity (incrementing and decrementing), and motor unit derecruitment, phases that may be modified to maintain ventilation across conditions. The primary goal of the present study was to investigate the effects of hypercapnia, which increases respiratory rate and tidal volume, on DIAm neuromotor control in awake rats. We recorded DIAm electromyography (EMG) using implanted chronic fine-wire electrodes in nine Sprague-Dawley rats during normocapnia and hypercapnia (7% CO2). The durations of motor unit recruitment/derecruitment were estimated by evaluating stationarity of DIAm EMG activity during normocapnia and hypercapnia; the motor unit recruitment/derecruitment durations were used to evaluate RMS EMG recruitment/derecruitment amplitudes. Overall, hypercapnia reduced the burst duration by ~40% and increased respiratory rate by ~50%. The change in the burst duration was primarily attributable to a 57% decrease in the peak-to-offset duration of the DIAm RMS EMG signal, suggesting a suppression of postinspiratory activity. Although neither the recruitment duration nor the onset-to-peak duration changed with hypercapnia, both the recruitment and peak amplitudes increased by 11% and 23% respectively. Therefore, although hypercapnia increases the number of motor units being recruited and their discharge rates, ventilation is primarily increased by increasing respiratory rate. Additionally, hypercapnia eliminated the decrementing sustained activity phase and consequently increased derecruitment amplitude by 171%. The results of the present study reveal that respiratory rate is increased chiefly by reducing the decrementing (i.e. "postinspiratory") phase of DIAm EMG activity.

A multimodal fusion network based on a cross-attention mechanism for the classification of Parkinsonian tremor and essential tremor.

Parkinsonian tremor (PT) and Essential tremor (ET) exist as upper limb tremors in clinical practice. Notably, their types of trembling share similar presentations and overlapping frequencies. To enhance objectivity and efficiency in the diagnosis of these two diseases, there is a pressing need for more objective tremor classification procedures. This study proposes a novel multimodal fusion network based on a cross-attention mechanism (MFCA-Net) to automate the classification of upper limb tremors between PTs and ETs. To this end, 140 patients with PTs and ETs were recruited, and acceleration and surface electromyography (sEMG) signals were collected from the forearm during tremor episodes. To comprehensively capture the global and local features of input signals, a multiscale convolution in MFCA-Net was designed. Furthermore, the cross-attention mechanism was applied to fuse the features of the two input signals. The results demonstrate that the final classification accuracy exceeded 97.18% when MFCA-Net was used. Compared with the single acceleration signal and single sEMG signal inputs, the recognition accuracies increased by 18.91% and 10.04%, respectively. Therefore, the proposed MFCA-Net in this study serves as an objective and potential tool for assisting clinicians in the diagnosis of PT and ET patients.

Hybrid Functional Near-Infrared Spectroscopy System and Electromyography for Prosthetic Knee Control

The increasing number of individuals with limb loss worldwide highlights the need for advancements in prosthetic knee technology. To improve control and quality of life, integrating brain–computer communication with motor imagery offers a promising solution. This study introduces a hybrid system that combines electromyography (EMG) and functional near-infrared spectroscopy (fNIRS) to address these limitations and enhance the control of knee movements for individuals with above-knee amputations. The study involved an experiment with nine healthy male participants, consisting of two sessions: real execution and imagined execution using motor imagery. The OpenBCI Cyton board collected EMG signals corresponding to the desired movements, while fNIRS monitored brain activity in the prefrontal and motor cortices. The analysis of the simultaneous measurement of the muscular and hemodynamic responses demonstrated that combining these data sources significantly improved the classification accuracy compared to using each dataset alone. The results showed that integrating both the EMG and fNIRS data consistently achieved a higher classification accuracy. More specifically, the Support Vector Machine performed the best during the motor imagery tasks, with an average accuracy of 49.61%, while the Linear Discriminant Analysis excelled in the real execution tasks, achieving an average accuracy of 89.67%. This research validates the feasibility of using a hybrid approach with EMG and fNIRS to enable prosthetic knee control through motor imagery, representing a significant advancement potential in prosthetic technology.

Replay-Based Incremental Learning Framework for Gesture Recognition Overcoming the Time-Varying Characteristics of sEMG Signals

Gesture recognition techniques based on surface electromyography (sEMG) signals face instability problems caused by electrode displacement and the time-varying characteristics of the signals in cross-time applications. This study proposes an incremental learning framework based on densely connected convolutional networks (DenseNet) to capture non-synchronous data features and overcome catastrophic forgetting by constructing replay datasets that store data with different time spans and jointly participate in model training. The results show that, after multiple increments, the framework achieves an average recognition rate of 96.5% from eight subjects, which is significantly better than that of cross-day analysis. The density-based spatial clustering of applications with noise (DBSCAN) algorithm is utilized to select representative samples to update the replayed dataset, achieving a 93.7% recognition rate with fewer samples, which is better than the other three conventional sample selection methods. In addition, a comparison of full dataset training with incremental learning training demonstrates that the framework improves the recognition rate by nearly 1%, exhibits better recognition performance, significantly shortens the training time, reduces the cost of model updating and iteration, and is more suitable for practical applications. This study also investigates the use of the incremental learning of action classes, achieving an average recognition rate of 88.6%, which facilitates the supplementation of action types according to the demand, and further improves the application value of the action pattern recognition technology based on sEMG signals.

Multilevel attention mechanism for motion fatigue recognition based on sEMG and ACC signal fusion.

This study aims to develop a cost-effective and reliable motion monitoring device capable of comprehensive fatigue analysis. It achieves this objective by integrating surface electromyography (sEMG) and accelerometer (ACC) signals through a feature fusion strategy. The study introduces a multi-level attention mechanism for classification, leveraging convolutional neural networks (CNNs). The preprocessing phase involves a local feature attention mechanism that enhances local waveform features using the amplitude envelope. A dual-scale attention mechanism, operating at both channel and neuron levels, is employed to enhance the model's learning from high-dimensional fused data, improving feature extraction and generalization. The local feature attention mechanism significantly improves the model's classification accuracy and convergence, as demonstrated in ablation experiments. The model, optimized with multi-level attention mechanisms, excels in accuracy and generalization, particularly in handling data with pseudo-artifacts. Computational analysis indicates that the proposed optimization algorithm has minimal impact on CNN's training and testing times. The study achieves recognition accuracies of 92.52%, 92.38%, and 92.30%, as well as F1-scores of 91.92%, 92.13%, and 92.29% for the three fatigue states, affirming its reliability. This research provides technical support for the development of affordable and dependable wearable motion monitoring devices.

Comparison of synergy extrapolation and static optimization for estimating multiple unmeasured muscle activations during walking

BackgroundCalibrated electromyography (EMG)-driven musculoskeletal models can provide insight into internal quantities (e.g., muscle forces) that are difficult or impossible to measure experimentally. However, the need for EMG data from all involved muscles presents a significant barrier to the widespread application of EMG-driven modeling methods. Synergy extrapolation (SynX) is a computational method that can estimate a single missing EMG signal with reasonable accuracy during the EMG-driven model calibration process, yet its performance in estimating a larger number of missing EMG signals remains unknown.MethodsThis study assessed the accuracy with which SynX can use eight measured EMG signals to estimate muscle activations and forces associated with eight missing EMG signals in the same leg during walking while simultaneously performing EMG-driven model calibration. Experimental gait data collected from two individuals post-stroke, including 16 channels of EMG data per leg, were used to calibrate an EMG-driven musculoskeletal model, providing “gold standard” muscle activations and forces for evaluation purposes. SynX was then used to predict the muscle activations and forces associated with the eight missing EMG signals while simultaneously calibrating EMG-driven model parameter values. Due to its widespread use, static optimization (SO) applied to a scaled generic musculoskeletal model was also utilized to estimate the same muscle activations and forces. Estimation accuracy for SynX and SO was evaluated using root mean square errors (RMSE) to quantify amplitude errors and correlation coefficient r values to quantify shape similarity, each calculated with respect to “gold standard” muscle activations and forces.ResultsOn average, compared to SO, SynX with simultaneous model calibration produced significantly more accurate amplitude and shape estimates for unmeasured muscle activations (RMSE 0.08 vs. 0.15, r value 0.55 vs. 0.12) and forces (RMSE 101.3 N vs. 174.4 N, r value 0.53 vs. 0.07). SynX yielded calibrated Hill-type muscle–tendon model parameter values for all muscles and activation dynamics model parameter values for measured muscles that were similar to “gold standard” calibrated model parameter values.ConclusionsThese findings suggest that SynX could make it possible to calibrate EMG-driven musculoskeletal models for all important lower-extremity muscles with as few as eight carefully chosen EMG signals and eventually contribute to the design of personalized rehabilitation and surgical interventions for mobility impairments.

Reducing electrocardiographic interference in the multichannel electromyogram to help muscle fatigue assessment in low-intensity contractions.

Surface electromyography (EMG) can be used in the rehabilitation of musculoskeletal disorders, for evaluating the coordination of muscles that stabilize a joint, or as an objective tool in assessing muscle fatigue. This latter may be achieved by evaluating the low-frequency content of the EMG. However, the electrocardiogram (ECG) interference is also recorded when EMG is acquired close to the heart. It may mask or even modify the information of interest in the EMG. Different signal processing techniques have been proposed to eliminate ECG artifacts from the EMG signals, the high-pass filter being the most used one. Nevertheless, this classic filtering approach would also attenuate EMG activities below 30 Hz, which contain information from low-intensity muscle contractions. This work addresses the automatic ECG attenuation when multichannel EMG is collected on the left pectoralis major muscle during a muscle fatigue test. The proposed ECG artifact mitigation extends a successful automatic template subtraction (TS) approach, which does not require an extra reference channel and is now applied to independent EMG source signal components extracted using a blind source separation technique (ICA, Independent Component Analysis). The automatic detection of ECG components was performed through two alternative measures: entropy and Kullback-Leibler divergence. While the ECG interference seems to hamper the detection of muscle fatigue in the low-contraction regime, the association ICA+TS preserved better the EMG low-frequency content, and the entropy-based automatic detection was found to be more suitable, avoiding possible errors that might arise from manual detection procedures.

Identification of muscle-activation-dependent human-exoskeleton coupling parameters

This paper proposed a muscle-activation-dependent human-exoskeleton model for predicting human-exoskeleton coupling parameters to improve the studies of coupling dynamics. With a newly designed platform and the help of 20 volunteers (10 males and 10 females, age: 24.45 ± 2.31 years old, height: 167.70 ± 8.35 cm, weight: 66.50 ± 18.74 kg), coupling parameters were identified with surface electromyographic (EMG) signals monitored to represent muscle activation. Then convolutional neural network (CNN) was used to predict coupling parameters with six EMG features as inputs:mean absolute value (MAV), mean absolute value slope (MAVSLP), waveform length (WL), Willison Amplitude (WAMP), variance (VAR), and auto regressive (AR) coefficients. Finally, sensitivity analysis of the CNN’s performance identified AR, MAV, and VAR as the key determinants of the coupling parameters. Further analysis unveiled strong correlation between coupling stiffness and both MAV and VAR. The novelty and contribution are the design of coupling experimental platform and the establishment of muscle-activation-dependent human-exoskeleton coupling model which provides a possibility to obtain coupling parameter identification form complex human-exoskeleton interaction scenarios.

Predicting Continuous Locomotion Modes via Multidimensional Feature Learning From sEMG.

Walking-assistive devices require adaptive control methods to ensure smooth transitions between various modes of locomotion. For this purpose, detecting human locomotion modes (e.g., level walking or stair ascent) in advance is crucial for improving the intelligence and transparency of such robotic systems. This study proposes Deep-STF, a unified end-to-end deep learning model designed for integrated feature extraction in spatial, temporal, and frequency dimensions from surface electromyography (sEMG) signals. Our model enables accurate and robust continuous prediction of nine locomotion modes and 15 transitions at varying prediction time intervals, ranging from 100 to 500 ms. Experimental results showcased Deep-STP's cutting-edge prediction performance across diverse locomotion modes and transitions, relying solely on sEMG data. When forecasting 100 ms ahead, Deep-STF achieved an improved average prediction accuracy of 96.60%, outperforming seven benchmark models. Even with an extended 500ms prediction horizon, the accuracy only marginally decreased to 93.22%. The averaged stable prediction times for detecting next upcoming transitions spanned from 31.47 to 371.58 ms across the 100-500 ms time advances. Although the prediction accuracy of the trained Deep-STF initially dropped to 71.12% when tested on four new terrains, it achieved a satisfactory accuracy of 92.51% after fine-tuning with just 5 trials and further improved to 96.27% with 15 calibration trials. These results demonstrate the remarkable prediction ability and adaptability of Deep-STF, showing great potential for integration with walking-assistive devices and leading to smoother, more intuitive user interactions.

Design of a supersoft, ultra-stretchable, and 3D printable hydrogel electrical bioadhesive interface for electromyography monitoring

Electromyography (EMG) monitoring has been extensively employed for critical applications in medicine, sports science, and rehabilitation. However, the mechanical mismatch between conventional EMG electrodes and the skin can lead to electrode detachment upon significant skin deformation. To address this limitation, we develop a PEDOT:PSS-based hydrogel electrical bioadhesive interface (EBI) that incorporates molecular doping and robust adhesion strategies to achieve excellent mechanical compatibility with biological tissues. This hydrogel EBI is fabricated using direct writing of printable inks followed by in-situ thermal initiation, enabling the creation of customizable patterns with high shape fidelity. The resultant 3D-printed PEDOT:PSS-based hydrogel EBI exhibits supersoft properties (Young's modulus 5-8.5 kPa), ultra-stretchability (1,175% strain), robust adhesion (>133 kPa), and outstanding electrochemical performance (CIC reduction by 0.45% over 100,000 cycles). Additionally, we further develop a PEDOT:PSS-based hydrogel electrode specifically for stable EMG signal recording. This electrode outperforms superior signal-to-noise ratio (SNR) performance compared to commercial electrodes in EMG monitoring.

Effects of robot-assisted hand function therapy on brain functional mechanisms: a synchronized study using fNIRS and sEMG.

Robot-assisted hand function therapy is pivotal in the rehabilitation of patients with stroke; however, its therapeutic mechanism remains elusive. Currently, research examining the impact of robot-assisted hand function therapy on brain function in patients with stroke is scarce, and there is a lack of studies investigating the correlation between muscle activity and alterations in brain function. This study aimed to investigate the correlation between forearm muscle movement and brain functional activation by employing the synchronized use of functional near-infrared spectroscopy and surface electromyography methods. Moreover, it sought to compare neural activity patterns during different rehabilitation tasks and refine the mechanism of robot-assisted hand function therapy for post-stroke hand function impairments. Stroke patients with hand dysfunction underwent three sessions of robot-assisted hand function therapy within 2 weeks to 3 months of onset. The fNIRS-sEMG synchronous technique was used to observe brain function and forearm muscle activation. Ten participants were randomly assigned to receive mirror, resistance, or passive rehabilitation training. During the intervention, cortical and muscle activation information was obtained using fNIRS and electromyographic signals. The primary outcomes included changes in oxyhemoglobin concentration and root mean square of surface electromyography. Compared to the resting state, the Oxy-Hb concentration in the brain regions involved in three rehabilitation tasks with robot-assisted hand function therapy significantly increased (p < 0.05). Mirror therapy significantly enhanced the prefrontal cortex and the superior frontal cortex activation levels. In contrast, resistance therapy significantly promoted the activation of the supplementary motor area and the premotor cortex. Passive rehabilitation tasks showed some activation in the target brain area premotor cortex region. Robot-assisted hand function therapy has shown that forearm muscle movement is closely related to oxygenated hemoglobin concentration activity in specific brain regions during different rehabilitation tasks. The simultaneous sEMG-fNIRS study found a significant correlation between muscle movement and brain activity after stroke, which provides an important basis for understanding the treatment mechanism of hand function impairment.

AUTOMATED GESTURE RECOGNITION USING APPLIED LINGUISTICS WITH DATA-DRIVEN DEEP LEARNING FOR ARABIC SPEECH TRANSLATION

Gesture recognition for Arabic speech translation includes developing advanced technologies that correctly translate body and hand movements corresponding to Arabic sign language (ArSL) into spoken Arabic. This leverages machine learning and computer vision techniques in complex systems simulation platforms to scrutinize the gestures utilized in ArSL, detecting mild differences in facial expressions, hand shapes, and movements. Sign Language Recognition (SLR) is paramount in assisting communication for the Deaf and Hard-of-Hearing communities. It includes using vision-based methods and Surface Electromyography (sEMG) signals. The sEMG signal is crucial for recognizing hand gestures and capturing muscular activities in sign language. Researchers have comprehensively shown the capability of EMG signals to approach specific details, mainly in classifying hand gestures. This progression is a stimulating feature in extracting the interpretation and recognition of sign languages and investigating the phonology of signed language. Leveraging machine learning algorithms and signal processing techniques in complex systems simulation platforms, researchers aim to extract relevant traits from the sEMG signals that correspond to different ArSL gestures. This study introduces an Enhanced Dwarf Mongoose Algorithm with a Deep Learning-Driven Arabic Sign Language Detection (EDMODL-ASLD) technique on sEMG data. In the initial phase, the presented EDMODL-ASLD model is subjected to data preprocessing to change the input sEMG data into an attuned format. In the next stage, feature extraction with fractal theories is used to gather relevant and nonredundant data from the EMG window to construct a feature vector. In this study, the absolute envelope (AE), energy (E), root-mean square (RMS), standard deviation (STD), and mean absolute value (MAV) are the five time-domain extracted features for the EMG window observation. Meanwhile, the dilated convolutional long short-term memory (ConvLSTM) technique is used to identify distinct sign languages. To improve the results of the dilated ConvLSTM model, the hyperparameter selection process is executed using the EDMO model. To illustrate the significance of the EDMODL-ASLD technique, a brief experimental validation is made on the Arabic SLR database. The experimental validation of the EDMODL-ASLD technique portrayed a superior accuracy value of 96.47% over recent DL approaches.

Changes in motor unit firing pattern are associated with post-exercise blood pressure response in older untreated but not treated hypertensive adults.

This study aimed to determine the difference in motor unit (MU) firing pattern between hypertensive and normotensive individuals, and the relationship between MU firing pattern and post-exercise blood pressure (BP) response in older individuals. Fourteen older untreated (systolic/diastolic BP (SBP/DBP)≥130/80mmHg, 76 (5) years), 11 treated hypertensive (78 (4) years) and 14 normotensive (SBP/DBP<130/80mmHg, 71 (4) years) individuals were studied. Participants performed ramp-up exercises until 50% of maximal voluntary contraction (MVC) of knee extension and five MVCs. During the ramp-up exercise, high-density surface electromyography signals were recorded and each MU firing rate (FR) and recruitment threshold was assessed. The slope of the linear regression between MUFRs and recruitment thresholds was calculated to assess the MU firing pattern. Pre- and post-exercise blood pressure was measured. Change in (∆)SBP from pre- to post-exercise was greater in treated hypertensive than untreated hypertensive individuals (P=0.026). MUFR was lower in treated hypertensive than untreated hypertensive and normotensive individuals (P<0.001). Although the slope was not significantly different between groups (P=0.294), FRs of larger MUs were lower than those of smaller MUs in treated hypertensive and normotensive individuals (P<0.05) but sustained in untreated hypertensive individuals. The FRs of larger MUs and slope were positively correlated with the ∆SBP only in hypertensive individuals (r=0.768 and 0.715; P=0.044 and 0.020). MUFR was lower in treated hypertensive than untreated hypertensive and normotensive individuals. Furthermore, MU firing patterns were associated with the ∆SBP after exercise in older untreated hypertensive individuals, but this relationship was not observed in treated hypertensive and normotensive individuals.

Comparison Between InterCriteria and Correlation Analyses over sEMG Data from Arm Movements in the Horizontal Plane

InterCriteria analysis (ICrA) and two kinds of correlation analyses, Pearson (PCA) and Spearman (SCA), were applied to surface electromyography (sEMG) signals obtained from human arm movements in the horizontal plane. Ten healthy participants performed ten movements, eight of which were cyclic. Each cyclic movement (CM) consisted of flexion and extension phases with equal duration (10 s, 6 s, 2 s, and 1 s) and two 5 s rest poses between them. The CMs were performed with and without an added load of 0.5 kg on the wrists of the participants. The sEMG signals from six different muscles or separate muscle heads (m. deltoideus pars clavicularis, m. deltoideus pars spinata, m. brachialis, m. anconeus, m. biceps brachii, and m. triceps brachii long head) were recorded and used to compare the results of the ICrA, PCA, and SCA. All three methods found identical consonance pairs for the flexion and extension CM phases. Additionally, PCA detected two more consonance pairs in the extension phases. In this investigation, ICrA, PCA, and SCA were proven to be reliable tools when applied separately or in combination for sEMG data. These three methods are appropriate for researching arm movements in the horizontal plane and experimental protocol revision.

The importance of analysing a single common window of a concentric and eccentric dynamic muscle contraction: unveiling the components of EMG data and motor unit pool

This study proposes a method for analysing decomposed electromyographic signals to enhance the characterization of motor units (MU). It involves two steps: (i) clustering groups of motor units based on firing rate (FR), recruitment threshold, and MU action potential amplitude, and (ii) segmentation of data at the time of interest. Such method, capable of distinguishing different groups of MU, could help understanding muscle force production. FR of MU in Vastus Lateralis and Rectus Femoris during knee extension was investigated. The findings distinguish MU groups and reveals differences in: FR between both contractions types; clustered groups; and segmented MU data in both contraction types.

Compliant Grasp Control Method for the Underactuated Prosthetic Hand Based on the Estimation of Grasping Force and Muscle Stiffness with sEMG

Human muscles can generate force and stiffness during contraction. When in contact with objects, human hands can achieve compliant grasping by adjusting the grasping force and the muscle stiffness based on the object’s characteristics. To realize humanoid-compliant grasping, most prosthetic hands obtain the stiffness parameter of the compliant controller according to the environmental stiffness, which may be inconsistent with the amputee’s intention. To address this issue, this paper proposes a compliant grasp control method for an underactuated prosthetic hand that can directly obtain the control signals for compliant grasping from surface electromyography (sEMG) signals. First, an estimation method of the grasping force is established based on the Huxley muscle model. Then, muscle stiffness is estimated based on the muscle contraction principle. Subsequently, a relationship between the muscle stiffness of the human hand and the stiffness parameters of the prosthetic hand controller is established based on fuzzy logic to realize compliant grasp control for the underactuated prosthetic hand. Experimental results indicate that the prosthetic hand can adjust the desired force and stiffness parameters of the impedance controller based on sEMG, achieving a quick and stable grasp as well as a slow and gentle grasp on different objects.

Rehabilitation exoskeleton system with bidirectional virtual reality feedback training strategy

AbstractVirtual reality (VR) technology revitalises rehabilitation training by creating rich, interactive virtual rehabilitation scenes and tasks that deeply engage patients. Robotics with immersive VR environments have the potential to significantly enhance the sense of immersion for patients during training. This paper proposes a rehabilitation robot system. The system integrates a VR environment, the exoskeleton entity, and research on rehabilitation assessment metrics derived from surface electromyographic signal (sEMG). Employing more realistic and engaging virtual stimuli, this method guides patients to actively participate, thereby enhancing the effectiveness of neural connection reconstruction—an essential aspect of rehabilitation. Furthermore, this study introduces a muscle activation model that merges linear and non‐linear states of muscle, avoiding the impact of non‐linear shape factors on model accuracy present in traditional models. A muscle strength assessment model based on optimised generalised regression (WOA‐GRNN) is also proposed, with a root mean square error of 0.017,347 and a mean absolute percentage error of 1.2461%, serving as critical assessment indicators for the effectiveness of rehabilitation. Finally, the system is preliminarily applied in human movement experiments, validating the practicality and potential effectiveness of VR‐centred rehabilitation strategies in medical recovery.

Dynamical network-based evaluation for neuromuscular dysfunction in stroke-induced hemiplegia during standing

BackgroundA given movement requires precise coordination of multiple muscles under the control of center nervous system. However, detailed knowledge about the changing characteristics of neuromuscular control for multi-muscle coordination in post-stroke hemiplegic patients during standing is still lacking. This study aimed to investigate the hemiplegia-linked neuromuscular dysfunction during standing from the perspective of multi-muscle dynamical coordination by utilizing a novel network approach – weighted recurrence network (WRN).MethodsTen male hemiplegic patients with first-ever stroke and 10 age-matched healthy male adults were instructed to stand on a platform quietly for 30 s with eyes opened and eyes closed, respectively. The WRN was constructed based on the surface electromyography signals of 16 muscles from trunk, hips, thighs and calves. Relevant topological parameters, including clustering coefficient (C) and average shortest path length (L), were extracted to evaluate the dynamical coordination of multiple muscles. A measure of node centrality in network theory, degree of centrality (DC), was innovatively introduced to assess the contribution of single muscle in the multi-muscle dynamical coordination. The standing-related assessment metric, center of pressure (COP), was provided by the platform directly.ResultsResults showed that the post-stroke hemiplegic patients stood with remarkably higher similarity of muscle activation and more coupled intermuscular dynamics, characterized by higher C and lower L than the healthy subjects (p < 0.05). The DC values and rankings of back, hip and calf muscles on the affected side were significantly decreased, whereas those on the unaffected side were significantly increased in hemiplegia group compared with the healthy group (p < 0.05). Without visual feedback, subjects exhibited enhanced muscle coordination and increased muscle involvement (p < 0.05). A decrease in C and an increase in L of WRN were observed with decreased COP areas (p < 0.05).ConclusionsThese findings revealed that stroke-induced hemiplegia could significantly influence the neuromuscular control, which was manifested as more coupled intermuscular dynamics, abnormal deactivation of muscles on affected side and compensation of muscles on unaffected side from the perspective of multi-muscle coordination. Enhanced multi-muscle dynamical coordination was strongly associated with impaired postural control. This study provides a novel analytical tool for evaluation of neuromuscular dysfunction and specification of responsible muscles for impaired postural control in stroke-induced hemiplegic patients, and could be potentially applied in clinical practice.

Development of an Electromyography Signal Acquisition Prototype and Statistical Validation Against a Commercial Device.

Electromyography (EMG) stands out as an accessible and inexpensive method for identifying muscle contractions on the surface and within deeper muscle tissues. Using specialized electronic circuits for amplification and filtering can help develop simple but effective systems for detecting and analyzing these signals. However, EMG devices developed by research teams frequently lack rigorous methodologies for validating the quality of the signals they record compared to those obtained by commercial systems that have undergone extensive testing and regulatory approval for market release. This underscores the critical need for standardized validation techniques to reliably assess the performance of experimental devices relative to established commercial equipment. Hence, this study introduces a methodology for the development and statistical validation of a laboratory EMG circuit compared with a professional device available on the market. The experiment simultaneously recorded the muscle electrical activity of 18 volunteers using two biosignal acquisition devices-a prototype EMG and a commercial system-both applied in parallel at the same recording site. Volunteers performed a series of finger and wrist extension movements to elicit myoelectric activity in these forearm muscles. To achieve this, it was necessary to develop not only the EMG signal conditioning board, but also two additional interface boards: one for enabling parallel recording on both devices and another for synchronizing the devices with the task programmatically controlled in Python that the volunteers were required to perform. The EMG signals generated during these tasks were recorded simultaneously by both devices. Subsequently, 22 feature indices commonly used for classifying muscular activity patterns were calculated from two-second temporal windows of the recordings to extract detailed temporal and spatial characteristics. Finally, the Mean Absolute Percentage Error (MAPE) was computed to compare the indices from the prototype with those from the commercial device, using this method as a validation system to assess the quality of the signals recorded by the prototype relative to the commercial equipment. A concordance of 87.6% was observed between the feature indices calculated from the recordings of both devices, suggesting high effectiveness and reliability of the EMG signals recorded by the prototype compared to the commercial device. These results validate the efficacy of our EMG prototype device and provide a solid foundation for the future evaluation of similar devices, ensuring their reliability, accuracy, and suitability for research or clinical applications.

Influence of Anesthesiology Protocol on the Quality of Intraoperative Nerve Monitoring During Thyroid Surgery, One-Year Single Center Experience.

Anesthesia plays a very important role in the successful management of intraoperative neuromonitoring (IONM). The aim of our study was to investigate the impact of anesthesia induction and maintenance on the quality of signals during surgeries on the thyroid and parathyroid glands using neuromonitoring. The study included 72 patients who underwent surgery with IONM for one year. All the patients were intubated using a Glidescope videolaryngoscope with a hyperangulated blade. Two different approaches were used to facilitate intubation: succinylcholine-1 mg/kg and rocuronium bromide-0.3 mg/kg. For anesthesia maintenance, total intravenous anesthesia (TIVA) or combined anesthesia was used. Patients' body movements during operations, as well as electromyography signals from the vagus and recurrent laryngeal nerves before resection, were recorded as V1 and R1. Intraoperative unwanted movements were recorded in 25% of patients. Undesired movements were more frequently recorded in the TIVA group compared to the combined anesthesia group (p < 0.001) as well as in patients who received succinylcholine compared to patients who received rocuronium bromide (p = 0.028). Type of anesthesia maintenance as well as type of muscle relaxant did not affect the quality of recorded nerve signals. (p = 0.169 and p = 0.894, respectively). The type of muscle relaxant used significantly affects the occurrence of undesirable movements during thyroid surgery with IONM, while the type of anesthesia maintenance did not influence either the quality of the obtained signal or the occurrence of undesirable movements.