4.7 Article

Segment-Wise Decomposition of Surface Electromyography to Identify Discharges Across Motor Neuron Populations

Related references

Note: Only part of the references are listed.
Article Engineering, Biomedical

Adaptive Real-Time Decomposition of Electromyogram During Sustained Muscle Activation: A Simulation Study

Yang Zheng et al.

Summary: An adaptive real-time decomposition approach has been developed for prolonged muscle activation. It increases the identifiable motor unit (MU) number and improves decomposition accuracy by periodically optimizing and updating the separation matrix. This approach allows for longitudinal evaluation of MU firing and recruitment properties and enhances neural decoding performance.

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING (2022)

Article Neurosciences

Less common synaptic input between muscles from the same group allows for more flexible coordination strategies during a fatiguing task

Julien Rossato et al.

Summary: The study aimed to investigate the redistribution of neural drive between muscles during a fatiguing contraction and its relation to the initial level of common synaptic input. The results indicate that less common input between muscles allows for more flexible coordination strategies, resulting in differential changes in neural drive across muscles.

JOURNAL OF NEUROPHYSIOLOGY (2022)

Article Computer Science, Information Systems

Non-Invasive Analysis of Motor Unit Activation During Simultaneous and Continuous Wrist Movements

Chen Chen et al.

Summary: This study proposed a noninvasive framework to analyze motor unit activities and estimate wrist torques during simultaneous contractions of multiple DoFs. The results demonstrate the feasibility and superiority of the proposed framework in kinetics estimation of simultaneous movements, extending the potential applications of surface EMG decomposition in human-machine interfaces.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2022)

Article Engineering, Biomedical

Skilled independent control of individual motor units via a non-invasive neuromuscular-machine interface

Emanuele Formento et al.

Summary: The study demonstrates that a non-invasive neuromuscular-machine interface providing real-time neurofeedback enables independent control of motor units suitable for high-performance BMI applications. Participants learned to skillfully control three biceps brachii motor units and showed the potential of this approach.

JOURNAL OF NEURAL ENGINEERING (2021)

Article Engineering, Biomedical

Deep Learning for Robust Decomposition of High-Density Surface EMG Signals

Alexander Kenneth Clarke et al.

Summary: The study explores the use of blind source separation algorithms to decompose HD-sEMG signals and predicts MU activations in a supervised learning framework using a GRU network, outperforming traditional gCKC. Experimental validation shows that GRU performs better at low signal-to-noise ratios.

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING (2021)

Article Engineering, Biomedical

A convolutional neural network to identify motor units from high-density surface electromyography signals in real time

Yue Wen et al.

Summary: This research demonstrates the feasibility and validity of accurately identifying MU activity from HD-EMG using deep CNN, with investigation into the impact of window size and step size of input signals.

JOURNAL OF NEURAL ENGINEERING (2021)

Article Engineering, Biomedical

Simultaneous and proportional control of wrist and hand movements by decoding motor unit discharges in real time

Chen Chen et al.

Summary: The study extends offline EMG decomposition algorithm to real-time identification of motor unit activities and proposes a MU-based method for online control of multiple motor tasks. Experimental results show good accuracy in identifying MUs and highly correlated activation of specific motions, demonstrating superior performance compared to conventional myo-control methods.

JOURNAL OF NEURAL ENGINEERING (2021)

Article Computer Science, Information Systems

On the Reuse of Motor Unit Filters in High Density Surface Electromyograms Recorded at Different Contraction Levels

Aljaz Francic et al.

Summary: The study analyzed the efficiency of Motor Unit tracking across different contraction levels in various muscles, demonstrating higher efficiency in transferring MU filters from low to high contraction levels in simulated conditions and muscle-dependent efficiency in experimental conditions. The majority of MUs identified at lower contraction levels were successfully tracked at higher levels, with only slight decrease in Sensitivity, likely due to differences in muscle size and anatomy impacting MU filter transfer efficiencies.

IEEE ACCESS (2021)

Article Computer Science, Information Systems

On the Prediction of Motor Unit Filter Changes in Blind Source Separation of High-Density Surface Electromyograms During Dynamic Muscle Contractions

Matej Kramberger et al.

Summary: The study demonstrated that using the Kalman filter for MU filter prediction results in more precise tracking of MU firing during dynamic contractions compared to the original CKC method and cyclostationary CKC method. The Kalman-based approach showed superior sensitivity and precision in identifying MUs during fast biceps brachii contractions with full elbow flexion and extension.

IEEE ACCESS (2021)

Article Engineering, Biomedical

Hand gesture recognition based on motor unit spike trains decoded from high-density electromyography

Chen Chen et al.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2020)

Article Computer Science, Information Systems

Finger Joint Angle Estimation Based on Motoneuron Discharge Activities

Chenyun Dai et al.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2020)

Article Engineering, Biomedical

Adaptive Real-Time Identification of Motor Unit Discharges From Non-Stationary High-Density Surface Electromyographic Signals

Chen Chen et al.

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING (2020)

Article Multidisciplinary Sciences

Spinal motoneurons of the human newborn are highly synchronized during leg movements

A. Del Vecchio et al.

SCIENCE ADVANCES (2020)

Review Physiology

A review on crosstalk in myographic signals

Irsa Talib et al.

EUROPEAN JOURNAL OF APPLIED PHYSIOLOGY (2019)

Article Mathematical & Computational Biology

Non-invasive Decoding of the Motoneurons: A Guided Source Separation Method Based on Convolution Kernel Compensation With Clustered Initial Points

Mohammad Reza Mohebian et al.

FRONTIERS IN COMPUTATIONAL NEUROSCIENCE (2019)

Article Engineering, Biomedical

Real-time isometric finger extension force estimation based on motor unit discharge information

Yang Zheng et al.

JOURNAL OF NEURAL ENGINEERING (2019)

Article Engineering, Biomedical

Motor Unit Identification From High-Density Surface Electromyograms in Repeated Dynamic Muscle Contractions

Vojko Glaser et al.

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING (2019)

Review Computer Science, Information Systems

A Review on Electromyography Decoding and Pattern Recognition for Human-Machine Interaction

Miguel Simao et al.

IEEE ACCESS (2019)

Article Engineering, Biomedical

Decoding Motor Unit Activity From Forearm Muscles: Perspectives for Myoelectric Control

Tamas Kapelner et al.

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING (2018)

Article Engineering, Biomedical

A Novel Validation Approach for High-Density Surface EMG Decomposition in Motor Neuron Disease

Maoqi Chen et al.

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING (2018)

Article Engineering, Biomedical

Automatic Implementation of Progressive FastICA Peel-Off for High Density Surface EMG Decomposition

Maoqi Chen et al.

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING (2018)

Article Engineering, Biomedical

Man/machine interface based on the discharge timings of spinal motor neurons after targeted muscle reinnervation

Dario Farina et al.

NATURE BIOMEDICAL ENGINEERING (2017)

Article Engineering, Biomedical

A Novel Framework Based on FastICA for High Density Surface EMG Decomposition

Maoqi Chen et al.

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING (2016)

Article Engineering, Biomedical

Multi-channel intramuscular and surface EMG decomposition by convolutive blind source separation

Francesco Negro et al.

JOURNAL OF NEURAL ENGINEERING (2016)

Article Computer Science, Information Systems

Surface EMG Decomposition Based on K-means Clustering and Convolution Kernel Compensation

Yong Ning et al.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2015)

Article Engineering, Biomedical

Linear and Nonlinear Regression Techniques for Simultaneous and Proportional Myoelectric Control

J. M. Hahne et al.

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING (2014)

Review Biophysics

Blind source identification from the multichannel surface electromyogram

A. Holobar et al.

PHYSIOLOGICAL MEASUREMENT (2014)

Article Engineering, Biomedical

Real-Time Motor Unit Identification From High-Density Surface EMG

Vojko Glaser et al.

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING (2013)

Review Chemistry, Analytical

Surface Electromyography Signal Processing and Classification Techniques

Rubana H. Chowdhury et al.

SENSORS (2013)

Article Physiology

Motor Unit

C. J. Heckman et al.

COMPREHENSIVE PHYSIOLOGY (2012)

Review Clinical Neurology

Decoding the neural drive to muscles from the surface electromyogram

Dario Farina et al.

CLINICAL NEUROPHYSIOLOGY (2010)

Article Engineering, Biomedical

Experimental Analysis of Accuracy in the Identification of Motor Unit Spike Trains From High-Density Surface EMG

Ales Holobar et al.

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING (2010)

Article Engineering, Electrical & Electronic

Multichannel blind source separation using convolution kernel compensation

Ales Holobar et al.

IEEE TRANSACTIONS ON SIGNAL PROCESSING (2007)

Article Computer Science, Interdisciplinary Applications

Correlation-based decomposition of surface electromyograms at low contraction forces

A Holobar et al.

MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING (2004)