Muscle activation and electromyography studies

NeuroMechanics: Electrophysiological and Computational Methods to Accurately Estimate the Neural Drive to Muscles in Humans In Vivo

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.

Quadriceps-hamstrings muscle co-activation during the swing phase of walking is modulated by task constraints in healthy adults

This study compares quadriceps-hamstrings co-activation during walking and isolated leg-swinging in healthy adults, finding higher co-activation during walking, particularly in mid- and terminal swing.

A generic noninvasive neuromotor interface for human-computer interaction

A wrist-worn surface electromyography interface and generic neural network decoders translate neuromotor signals into computer input across uncalibrated users, with up to 30% higher bandwidth after personalization.

Wearable Neural Interfaces: Real-Time Identification of Motor Neuron Discharges in Dynamic Motor Tasks

This paper introduces an adaptive EMG decomposition algorithm for real-time motor neuron discharge identification during dynamic movements, showing improved performance in simulated and experimental data.

One-Shot Random Forest Model Calibration for Hand Gesture Decoding

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.

Artificial physics engine for real-time inverse dynamics of arm and hand movement

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

Motor unit discharge rate modulation during isometric contractions to failure is intensity and task dependent

This study examines how motor unit discharge rate (MUDR) changes during isometric contractions to failure, showing task and intensity-dependent patterns.

Larger and denser: an optimal design for surface grids of EMG electrodes to identify greater and more representative samples of motor units

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

Two motor neuron synergies, invariant across ankle joint angles, activate the triceps surae during plantarflexion

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

High-frequency cortical neural inputs to muscles during movement cancellation

This study investigates high-frequency cortical inputs to muscles during movement cancellation, finding increased beta and gamma oscillations in brain and muscle activity during NO-GO trials without behavioral changes.

Mechanical Hand Synergies during Dynamic Hand Movements are Mostly Controlled in a Non-Synergistic Way by Spinal Motor Neurons

The paper investigates motor unit control during hand movements, finding that synergistic grasps are controlled non-synergistically by distinct motor units.

Too Much Information Is No Information: How Machine Learning and Feature Selection Could Help in Understanding the Motor Control of Pointing

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

Estimates of persistent inward currents in lower limb muscles are not different between inactive, resistance-trained and endurance-trained young individuals

Neuromechanical Justification of Lower-Limb Functional Tests for a Return to Running: A Muscle Coordination Analysis

This study identifies shared muscle synergies between running and functional tests for return to running, finding that walking, single leg hops, and step-up tests show higher synergy with running.

Reducing Motor Variability Enhances Myoelectric Control Robustness Across Limb Positions

This paper shows that reducing motor variability through training improves myoelectric control robustness across different limb positions.

Speed-dependent optimization of gravity effects for motor control

The paper analyzes phasic EMG activity during arm movements to show how the CNS optimizes gravity effects across movement speeds and directions.

Spatial and temporal muscle synergies provide a dual characterization of low-dimensional and intermittent control of upper-limb movements

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.

Non-parametric Functional Muscle Network as a Robust Biomarker of Fatigue

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.

Toward a generalizable deep CNN for neural drive estimation across muscles and participants

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

Short latency stretch reflexes depend on the balance of activity in agonist and antagonist muscles during ballistic elbow movements

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.

Facilitation-inhibition control of motor neuronal persistent inward currents in young and older adults

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.

Neurostimulation Artifact Removal for Implantable Sensors Improves Signal Clarity and Decoding of Motor Volition

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

Flexible neural control of motor units

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

The effects of biological sex on estimates of persistent inward currents in the human lower limb

The paper quantifies sex differences in motor unit discharge rates and persistent inward current estimates in human lower limb muscles, finding higher {Delta}F in females.

Accurate measurement of inhibition and excitation in human motor pools during sensory stimulation

The paper shows that surface electromyography (sEMG) underestimates motor pool responses during sensory stimulation, affecting both inhibition and excitation, with inaccuracies beyond signal cancellation.

Estimation of the firing behaviour of a complete motoneuron pool by combining EMG signal decomposition and realistic motoneuron modelling

The paper proposes a method combining EMG signal decomposition and realistic motoneuron modeling to estimate the firing behavior of entire motoneuron pools in humans.

Interfacing Spinal Motor Units in Non-Human Primates Identifies a Principal Neural Component for Force Control Constrained by the Size Principle

The paper tracks motor units in non-human primates during force contractions, finding stable recruitment order and a dominant neural component for force control.

Independent synaptic inputs to motor neurons driving antagonist muscles

This study investigates whether co-contraction of antagonist muscles is controlled by a common synaptic input or independent inputs, finding separate neural pathways for torque and impedance modulation.

Sensing the Full Dynamics of the Human Hand with a Neural Interface and Deep Learning

This paper develops a deep learning method to predict hand kinematics and forces from EMG signals, showing accurate prediction of unseen movements and learned anatomical neural features.

The control and training of single motor units in isometric tasks are constrained by a common synaptic input signal

The paper shows that humans cannot easily control individual motor units independently, as their activation is governed by a shared input signal and changes in de-recruitment order.