Search Results (32)

Adolescent idiopathic scoliosis (AIS) is associated with central neural control and peripheral muscle abnormalities. However, dynamic cortico-muscular coupling (CMC) during fatigue remains poorly understood. We used synchronous 32-channel electroencephalography and 32-channel high-density electromyography (HD-EMG) to record data from 20 adolescents with AIS and 20 healthy controls during the Biering–Sørensen test. Wavelet coherence and topographic mapping were used to characterize CMC and its spatial distribution. Descriptive analyses indicated group- and fatigue-related patterns in HD-EMG activation, cortical connectivity, and CMC topographies. For the prespecified C3/C4 regional analyses, effect sizes, 95% confidence intervals, exact p values, and false-discovery-rate-adjusted p values are reported to support transparent interpretation. These multimodal observations provide exploratory physiological patterns that require confirmation in larger studies with prespecified primary outcomes. Full article

Accurate gesture recognition is crucial for precision control of upper limb prostheses. High-density surface electromyography (HD-sEMG) enhances spatial resolution and information richness of human gesture representation, thus improving myoelectric control of bionic limbs. Recently, deep learning has been increasingly applied to HD-sEMG to enhance gesture recognition performance. However, the black-box nature of neural networks limits their interpretability and model optimization, hindering their practical application. In this paper, we developed a spatiotemporal dual-channel interpretable hybrid neural network (STDC-Net), and validated it using the Capgmyo DB-a dataset. STDC-Net uses Feature Channels and Spatial Channels to process the feature and spatial information of sEMG signals respectively for increased interpretability. SHapley Additive exPlanations (SHAP) values are used to rank the feature importance, aiding feature filtering and reducing the impact of irrelevant features. Graph attention layers are used to calculate the connections between each Spatial Channel, illustrating the relationships between channels. Our results demonstrated the superior performance of our STDC-Net compared to state-of-the-art (SOTA) methods. STDC-Net achieved 99.8% accuracy with a 150 ms sliding window, exceeding real-time implementation requirements for intra-subject tasks. It reached an accuracy of 97.33 ± 2.53% after fine-tuning for inter-subject tasks, outperforming the SOTA methods. Importantly, the SHAP value maps and the channel connection maps enhance the interpretability of the neural networks by offering detailed insights into the contribution of input features and parameter interactions of the network. These findings suggest STDC-Net holds significant promise for real-time prosthetic control. Full article

Objective: To compare the between-session reliability of motor units (MUs) behavior derived from high-density surface electromyography (HD-sEMG) during isometric knee extension across three analytical approaches: all identified MUs averaged per participant, tracked MUs averaged per participant, and tracked MUs using individual MUs as the statistical unit. Methods: Seventeen resistance-trained volunteers completed one familiarization and two identical experimental sessions. HD-sEMG signals were assessed in vastus lateralis at 30%, 50%, and 70% of maximum voluntary torque (MVT). Plateau discharge rate, recruitment discharge rate, and recruitment threshold (expressed in %MVT and Nm) were calculated. Reliability was quantified with the Standard error of the measurement (SEM), Coefficient of variation (CV), and Intraclass correlation coefficient (ICC). Results: All identified MUs averaged per participant (CV = 11.7%, ICC = 0.73) yielded comparable reliability than tracked MUs averaged per participant (CV = 13.7%, ICC = 0.68). Using the individual MU as the statistical unit produced the lowest reliability (CV = 16.6%, ICC = 0.58). Conclusions: Averaging all identified MUs per participant achieved reliability comparable to tracking, without the associated sample loss or workload, supporting it as the preferable analytical approach for training interventions. Full article

Background: We present a miniature (30 × 34 mm) 64-channel data acquisition headstage optimized for high-density surface electromyography. Methods: The headstage is made up of a multi-channel ASIC analogue front-end utilizing only MOS transistors, fabricated in 350 nm CMOS technology (IC die dimensions 6.9 × 1.8 mm), combined with an off-the-shelf multi-channel current-input ADC (DDC264, Texas Instruments). The ASIC analogue front-end employs MOS-based capacitors for both processing and AC-coupling. Results: The combination of these two sub-circuits enables the simultaneous recording of 64 channels at a typical sampling rate of 4 KHz with a maximum analogue bandwidth of 0.5–1500 Hz and a resolution of 20-bits. Typical input-referred-noise, determined by the analogue front-end, is 3.5 μV_RMS for a surface EMG bandwidth of interest of 20–500 Hz. This two-chip solution results in a power consumption of 5 mW per channel. Analogue performance variability of the custom ASIC was characterized across a dataset of 960-channels (15 dies) from two fabrication runs. Conclusions: This work practically demonstrates the viability of using both a MOS-only analogue front-end and commercially available off-shelf high-performance back-end hardware already developed for medical imaging applications to record high-density surface biosignals. The aforementioned techniques can be employed to reduce the size and cost for systems or wearable devices; facilitating the translation of high-density bio-acquisition setups from the research environment to more affordable commercial products. Full article

Transcutaneous spinal cord stimulation (tSCS) is a promising neuromodulation approach for motor recovery after spinal cord injury (SCI), yet clinical programming remains largely dependent on subjective parameter selection. This study evaluated high-density surface EMG (HD-sEMG)–derived spatial and temporal features as objective biomarkers for tSCS optimization in three adults with chronic cervical SCI. A 64-channel electrode array recorded stimulation-evoked responses across five cervical stimulation levels, four pulse widths, and graded amplitudes. Features describing activation magnitude, spatial distribution, cluster morphology, and temporal dynamics were extracted from epoch-based activation maps. Of the three enrolled participants, two demonstrated measurable stimulation-evoked responses and contributed to the paired-pulse analyses, whereas pulse-width analyses were limited to a single responsive muscle (left flexor carpi) in one participant. Paired-pulse analysis identified root mean square (RMS) as the most discriminative feature, revealing nonlinear, muscle- and level-specific dose–response relationships in which maximal suppression often occurred at intermediate rather than maximal amplitudes. Increasing pulse width expanded the spatial extent of recruitment (active area: p = 0.006; convex hull area: p = 0.004) without altering response timing. Polarity reversal analysis demonstrated stable innervation zone localization across stimulation levels and amplitudes. These findings establish a spatially resolved HD-sEMG framework that may support individualized tSCS parameter selection in SCI. Full article

The non-invasive recording of spinal cord neuronal activity, also known as electrospinography (ESG), using high-density surface electromyography (HD-sEMG) is a promising emerging biosensing modality. However, these recordings often contain electrocardiographic (ECG) artifacts that must be removed for accurate analysis. Given the emerging nature of ESG and the lack of dedicated signal processing methods, this study assesses the performance of seven established EMG denoising algorithms for their ability to preserve the broad spectral bandwidth needed for future ESG characterization: Template Subtraction (TS), Adaptive Template Subtraction (ATS), High-Pass Filtering at 200 Hz (HP200), ATS combined with HP200, Second-Order Extended Kalman Smoother (EKS2), Stationary Wavelet Transform (SWT), and Empirical Mode Decomposition (EMD). Performance was quantified using six metrics: Relative Error (RE), Signal-to-Noise Ratio (SNR), Cross-Correlation (CC), Spectral Distortion (SD), and Kurtosis Ratio (KR2) and its variation ($\Delta$KR2). ESG data were recorded from nine healthy participants at brachial and lumbar plexus sites with various electrode configurations. ATS consistently outperformed all other methods in suppressing cardiac artifacts of varying shapes. Although it did not fully preserve low-frequency content, ATS achieved the best balance between artifact removal and signal integrity. Algorithm performance improved when ECG contamination was lower, especially in brachial plexus recordings with closer reference electrodes. Full article

Control of elbow exoskeletons using muscular signals, although promising for the rehabilitation of millions of patients, has not yet been widely commercialized due to challenges in real-time intention estimation and management of dynamic uncertainties. From a practical perspective, millions of patients with stroke, spinal cord injury, or neuromuscular disorders annually require active rehabilitation, and elbow exoskeletons with precise and safe motion intention tracking capabilities can restore functional independence, reduce muscle atrophy, and lower treatment costs. In this research, an intelligent control framework was developed for an elbow joint exoskeleton, designed with the aim of precise and safe real-time tracking of the user’s motion intention. The proposed framework consists of two main stages: (a) real-time estimation of desired joint angle (as a proxy for movement intention) from High-Density Surface Electromyography (HD-sEMG) signals using an LSTM network and (b) implementation and comparison of three PID, impedance, and sliding mode controllers. A public EMG dataset including signals from 12 healthy individuals in four isometric tasks (flexion, extension, pronation, supination) and three effort levels (10, 30, 50 percent MVC) is utilized. After comprehensive preprocessing (Butterworth filter, 50 Hz notch, removal of faulty channels) and extraction of 13 time-domain features with 99 percent overlapping windows, the LSTM network with optimal architecture (128 units, Dropout, batch normalization) is trained. The model attained an RMSE of 0.630 Nm, R² of 0.965, and a Pearson correlation of 0.985 for the full dataset, indicating a 47% improvement in R² relative to traditional statistical approaches, where EMG is converted to desired angle via joint stiffness. An assessment of 12 motion–effort combinations reveals that the sliding mode controller consistently surpassed the alternatives, achieving the minimal tracking errors (average RMSE = 0.21 Nm, R² ≈ 0.96) and showing superior resilience across all tasks and effort levels. The impedance controller demonstrates superior performance in flexion/extension (average RMSE ≈ 0.22 Nm, R² > 0.94) but experiences moderate deterioration in pronation/supination under increased loads, while the classical PID controller shows significant errors (RMSE reaching 17.24 Nm, negative R² in multiple scenarios) and so it is inappropriate for direct myoelectric control. The proposed LSTM–sliding mode hybrid architecture shows exceptional accuracy, robustness, and transparency in real-time intention monitoring, demonstrating promising performance in offline simulation, with potential for real-time clinical applications pending hardware validation for advanced upper-limb exoskeletons in neurorehabilitation and assistive applications. Full article

Objectives: The present study compared the amplitude and spatial distribution of muscle excitation between a seated row performed with a fixed scapular position (fixed-SR) and a free scapular position (free-SR) in resistance-trained men, analyzing concentric and eccentric phases separately using high-density surface EMG (HD-sEMG). Methods: Fourteen resistance-trained males (age: 25 ± 4 years; stature: 1.74 ± 0.06 m; body mass: 76.22 ± 5.73 kg) performed fixed-SR and free-SR in a randomized cross-over design using 8-repetition maximum as the load for both variations. HD-sEMG grids recorded the activity from the upper/middle/lower trapezius, latissimus dorsi, lateral/posterior deltoid, biceps brachii, triceps brachii, and erector spinae. Normalized root mean squared (RMS) amplitude and excitation centroids in the mediolateral and craniocaudal planes were computed for the concentric and eccentric phases. Data were analyzed using repeated-measures statistical models, with significance set at p < 0.05. Results: During the concentric phase, nRMS amplitude was greater for the posterior deltoid in fixed-SR compared with free-SR (effect size [ES] = 0.66), whereas no between-condition difference was observed for the remaining muscles. During the eccentric phase, nRMS amplitude was greater in the fixed-SR for the middle trapezius (ES = 0.67) and the latissimus dorsi (ES = 0.85), with no between-condition differences detected for the remaining muscles. The centroid position analysis revealed that, during the eccentric phase, the middle trapezius centroid was located more laterally in the fixed-SR condition (ES = 0.54), while the posterior deltoid centroid was positioned more caudally in the fixed-SR compared with the free-SR condition (ES = 0.22). Conclusions: The fixed-SR and free-SR conditions produce comparable overall muscle excitation patterns, while showing some quantitative and spatial differences in selected upper-back muscles. These results suggest that scapular constraint influences the distribution of muscular excitation rather than overall excitation levels. Accordingly, both variations can be effectively used in resistance training, selecting to fix or free the scapulae depending on the emphasis on the scapular movements rather than a substantial difference in muscle excitation. Full article

Stroke-induced motor impairment necessitates objective and quantitative assessment tools for rehabilitation planning. In this study, a gesture-specific framework based on high-density surface electromyography (HD-sEMG) was developed to characterize neuromuscular dysfunction using eight macroscopic features and two microscopic motor unit decomposition features. HD-sEMG recordings were collected from stroke patients (n = 11; affected and unaffected sides) and healthy controls (n = 8; dominant side) during seven standardized hand gestures. Feature-level comparisons revealed hierarchical abnormalities, with the affected side showing significantly reduced activation/coordination relative to healthy controls, while the unaffected side exhibited intermediate deviations. For each gesture, dedicated K-nearest neighbors (KNN) models were constructed for clinical validation. For Brunnstrom stage classification, wrist extension yielded the best performance, achieving 92.08% accuracy and effectively discriminating severe (Stage 4), moderate (Stage 5), and mild (Stage 6) impairment as well as healthy controls. For fine motor recovery prediction, the thumb–index–middle finger pinch provided the optimal regression performance, predicting Upper Extremity Fugl–Meyer Assessment (UE-FMA) scores with R = 0.86 and RMSE = 3.24. These results indicate that gesture selection should be aligned with the clinical endpoint: wrist extension is most informative for gross recovery staging, whereas pinch gestures better capture fine motor control. Overall, the proposed HD-sEMG framework provides an objective approach for monitoring post-stroke recovery and supporting personalized rehabilitation assessment. Full article

The present study evaluates the effectiveness of a non-invasive wearable sensor system, combining accelerometers, surface electromyography, and artificial intelligence, to objectively characterize swallowing in elderly individuals affected by Parkinson’s Disease, without clinically manifested dysphagia. A cohort of patients and healthy control subjects performed the same swallowing test protocol, including tasks with different viscosity boluses, positioning a commercial adhesive grid of High-Density surface Electromyography (HD-sEMG) electrodes on the submental muscle and a triaxial accelerometer over the thyroid cartilage. Relevant temporal and spectral features were extracted from electromyography data. Proper filtering and processing by machine learning and Principal Component Analysis allowed identification of two distinct clusters of subjects, one predominantly composed of controls with just a few patients, the other mostly crowded by patients. Excellent classification performances were achieved (accuracy = 83.3%, precision = 79.0%, recall = 90.7%, F1-score = 84.5%, Cohen’s kappa = 0.67), revealing consistent differences in muscle activation patterns among subjects, even in the absence of clinically diagnosed dysphagia. These results support the feasibility of wearable sensor-based assessment as a reliable and non-invasive tool for the early detection of subclinical swallowing dysfunction in Parkinson’s Disease. Full article

Upper limb assistive exoskeletons help stroke patients by assisting arm movement in impaired individuals. However, effective control of these systems to help stroke survivors is a complex task. In this paper, a novel approach is proposed to enhance the control of upper limb assistive exoskeletons by using torque estimation and prediction in a proportional–integral–derivative (PID) controller loop to more optimally integrate the torque of the exoskeleton robot, which aims to eliminate system uncertainties. First, a model for torque estimation from Electromyography (EMG) signals and a predictive torque model for the upper limb exoskeleton robot for the elbow are trained. The trained data consisted of two-dimensional high-density surface EMG (HD-sEMG) signals to record myoelectric activity from five upper limb muscles (biceps brachii, triceps brachii, anconeus, brachioradialis, and pronator teres) during voluntary isometric contractions for twelve healthy subjects performing four different isometric tasks (supination/pronation and elbow flexion/extension) for one minute each, which were trained on long short-term memory (LSTM), bidirectional LSTM (BLSTM), and gated recurrent units (GRU) deep neural network models. These models estimate and predict torque requirements. Finally, the estimated and predicted torque from the trained network is used online as input to a PID control loop and robot dynamic, which aims to control the robot optimally. The results showed that using the proposed method creates a strong and innovative approach to greater independence and rehabilitation improvement. Full article

The study of muscle activation patterns using surface electromyography (sEMG) provides critical insights into muscle coordination, enabling advancements in prosthetics, robotics, and rehabilitation by improving intuitive control, replicating human movements, and developing targeted therapeutic strategies. The study involved 15 healthy participants aged 20–27, using Trigno Avanti sensors to record sEMG signals from forearm muscles during specific gestures, with data processed into activation maps to analyze muscle activity and coordination for applications in rehabilitation and prosthetics. The results revealed distinct muscle activation patterns for each gesture, highlighting precise muscle coordination, with specific muscles like m. flexor carpi ulnaris and m. extensor digitorum showing varying levels of involvement depending on the movement, while m. brachioradialis remained inactive across all gestures. The study’s findings enhance our understanding of motor control by revealing specific muscle activation patterns for different hand gestures, highlighting the selectivity of muscle coordination, and suggesting avenues for future research to improve prosthetic design and rehabilitation strategies. Full article

Several studies have shown interstitial cystitis/bladder pain syndrome (IC/BPS), a chronic condition that poses challenges in both diagnosis and treatment, is associated with painful pelvic floor muscles (PFM) and altered neural drive to these muscles. However, its pathophysiology could also involve other alterations in the electrical activity of PFM motor units (MUs). Studying these alterations could provide novel insights into IC/BPS and help its clinical management. This study aimed to characterize PFM activity at the MU level in women with IC/BPS and pelvic floor myalgia using high-density surface electromyography (HD-sEMG). Signals were recorded from 15 patients and 15 healthy controls and decomposed into MU action potential (MUAP) spike trains. MUAP amplitude, firing rate, and magnitude-squared coherence between spike trains were compared across groups. Results showed that MUAPs had significantly lower amplitudes during contractions on the patients’ left PFM, and delta-band coherence was significantly higher at rest on their right PFM compared to controls. These findings suggest altered PFM tissue and neuromuscular control in women with IC/BPS and pelvic floor myalgia. Our results demonstrate that HD-sEMG can provide novel insights into IC/BPS-related PFM dysfunction and biomarkers that help identify subgroups of IC/BPS patients, which may aid their diagnosis and treatment. Full article

Background. This study aims to determine if Riluzole usage can change the function and excitability of motor neurons. Methods. The clinical data and indices of motor neuron excitability were assessed using high-density surface EMG parameters from 80 ALS participants. The persistent inward current was assessed using the discharge rate from paired motor units obtained from the tibialis anterior muscle. This enabled the discharge rate at recruitment, peak discharge rates and the hysteresis of the recruitment–derecruitment frequencies (also known as delta F) to be calculated. Limbs were classified according to their strength. Results. No differences in these motor neuron discharge properties were found according to whether Riluzole was used. Conclusions. The possible interpretations of this finding are discussed. Full article

Background: Innovative algorithms for wearable devices and garments are critical for diagnosing and monitoring disease (such as lateral epicondylitis (LE)) progression. LE affects individuals across various professions and causes daily problems. Methods: We analyzed signals from the forearm muscles of 14 healthy controls and 14 LE patients using high-density surface electromyography. We discerned significant differences between groups by employing phase–amplitude coupling (PAC) features. Our study leveraged PAC, Daubechies wavelet with four vanishing moments (db4), and state-of-the-art techniques to train a neural network for the subject’s label prediction. Results: Remarkably, PAC features achieved 100% specificity and sensitivity in predicting unseen subjects, while state-of-the-art features lagged with only 35.71% sensitivity and 28.57% specificity, and db4 with 78.57% sensitivity and 85.71 specificity. PAC significantly outperformed the state-of-the-art features (adj. p-value < 0.001) with a large effect size. However, no significant difference was found between PAC and db4 (adj. p-value = 0.147). Also, the Jeffries–Matusita (JM) distance of the PAC was significantly higher than other features (adj. p-value < 0.001), with a large effect size, suggesting PAC features as robust predictors of neuromuscular diseases, offering a profound understanding of disease pathology and new avenues for interpretation. We evaluated the generalization ability of the PAC model using 99.9% confidence intervals and Bayesian credible intervals to quantify prediction uncertainty across subjects. Both methods demonstrated high reliability, with an expected accuracy of 89% in larger, more diverse populations. Conclusions: This study’s implications might extend beyond LE, paving the way for enhanced diagnostic tools and deeper insights into the complexities of neuromuscular disorders. Full article