Muscle activation and electromyography studies

The paper uses machine learning to analyze multimuscle activity during pointing movements, revealing modular tuning of muscles to direction and the role of deactivation in reducing effort.

This study links in vivo muscle dynamics to in situ force-length and force-velocity properties in guinea fowl lateral gastrocnemius, finding it operates at suboptimal lengths during force production.

This paper uses Bayesian methods to model muscle activation patterns during motion, comparing prior models with observed data to understand how the musculoskeletal system selects activation strategies.

This paper develops and validates a neuromusculoskeletal model with reflex-based muscle control to simulate sit-to-walk movements under varying conditions and study age-related compensation strategies.

The paper introduces MUedit, an open-source software for decomposing EMG signals and analyzing motor unit discharge timing, with simulated and experimental data for benchmarking.

This paper introduces a new set of evaluation metrics for myoelectric prosthesis controllers and demonstrates their use in comparing deep learning and traditional controllers.

This study examines how muscle length affects synaptic oscillations and force output in motor neurons, finding reduced alpha band oscillations with increased muscle length.

The paper describes a surgical technique to individually control rabbit lower hind-limb muscles and measures joint torque under different stimulation frequencies.

This study develops an adaptive real-time algorithm for decoding motor unit activity from HD-sEMG and shows it outperforms static methods under changing contraction conditions.

The paper describes new electrophysiological and computational methods to accurately estimate neural drive to muscles in humans in vivo by improving MU sampling and using data augmentation.

This study measures skill learning in closed-loop myoelectric prostheses using speed-accuracy tradeoffs to monitor progress and inform interface optimization.

This study investigates the relationship between residual force depression and muscle work during voluntary dorsiflexion contractions, finding that rFD is linearly related to MTU work but not fascicle work.

This study examines how vestibular signals modulate paraspinal muscle activity at different vertebral levels during walking, finding vertebral-specific responses and coordinated patterns.

This study assesses the inter-session reliability of quadriceps neuromuscular function measures in healthy females across age, showing excellent reliability and minimal systematic bias.

This paper develops a one-shot random forest model calibration method for hand gesture decoding using minimal user-specific EMG data, achieving higher accuracy than benchmark models.

This paper develops an artificial physics engine using neural networks to simulate inverse dynamics of arm and hand movement with low torque errors.

This paper develops a motoneuron-driven neuromuscular model that simulates human muscle contraction with motor unit resolution and subject-specific anatomy, validated against experimental force data.

This paper develops a method to accurately identify motoneuron discharges from ultrasound images across the full muscle cross-section, achieving high agreement with surface EMG decomposition.

This study validates blind source separation methods on ultrafast ultrasound images to identify motor unit activity using EMG data from eleven participants.

This paper shows that larger and denser EMG electrode grids improve the identification of more representative motor unit samples through computational and experimental analysis.

The paper identifies two motor neuron synergies in triceps surae muscles during plantarflexion that remain stable across ankle angles.

The paper introduces a Transformer-based method for sEMG signal analysis to improve movement recognition accuracy and generalization across datasets.

This study uses machine learning and feature selection to analyze high-dimensional EMG data and identify muscle activity patterns associated with different pointing angles.

The paper compares spatial and temporal muscle synergies in upper-limb movements, finding that temporal synergies require fewer components and show higher inter-subject similarity.

This paper introduces a non-parametric muscle network as a reliable biomarker for detecting fatigue using surface electromyography, showing significant decreases in network metrics compared to classical methods.

This paper introduces a deep CNN for estimating neural drive from HD-EMG signals, showing it generalizes across muscles, sessions, and participants without retraining.

This study examines how stretch reflex sensitivity in elbow muscles depends on agonist and antagonist muscle activity during movement and posture, finding a balance of control not observed during posture.

This study investigates how motor neuronal persistent inward currents (PICs) respond to neuromodulation and inhibition in young and older adults, finding preserved PIC facilitation but impaired deactivation in older adults during specific tasks.

This study evaluates two algorithms for removing neurostimulation artifacts from EMG signals, improving signal clarity and decoding of motor intention in amputees.

This study shows that motor units are flexibly controlled by the cortex, challenging the traditional view of common drive and rigid recruitment.