Highly Accurate Real-Time Decomposition of Single Channel Intramuscular EMG.

Objective: Real-time intramuscular electromyography (iEMG) decomposition, as an identification procedure of individual motor neuron (MN) discharge timings from a streaming iEMG recording, has the potential to be used in human-machine interfacing. However, for these applications, the decomposition accuracy and speed of current approaches need to be improved.

Methods: In our previous work, a real-time decomposition algorithm based on a Hidden Markov Model of EMG, using GPU-implemented Bayesian filter to estimate the spike trains of motor units (MU) and their action potentials (MUAPs), was proposed.

Simulation of Motor Unit Action Potential Recordings From Intramuscular Multichannel Scanning Electrodes.

Unlabelled: Multi-channel intramuscular EMG (iEMG) provides information on motor neuron behavior, muscle fiber (MF) innervation geometry and, recently, has been proposed as a means to establish a human-machine interface.

Objective: to provide a reliable benchmark for computational methods applied to such recordings, we propose a simulation model for iEMG signals acquired by intramuscular multi-channel electrodes.

Methods: we propose several modifications to the existing motor unit action potentials (MUAPs) simulation methods, such as farthest point sampling (FPS) for the distribution of motor unit territory centers in the muscle cross-section, accurate fiber-neuron assignment algorithm, modeling of motor neuron action potential propagation delay, and a model of multi-channel scanning electrode.

On-Line Recursive Decomposition of Intramuscular EMG Signals Using GPU-Implemented Bayesian Filtering.

Objective: Real-time intramuscular electromyography (iEMG) decomposition, which is needed in biofeedback studies and interfacing applications, is a complex procedure that involves identifying the motor neuron spike trains from a streaming iEMG recording.

Methods: We have previously proposed a sequential decomposition algorithm based on a Hidden Markov Model of EMG, which used Bayesian filter to estimate unknown parameters of motor unit (MU) spike trains, as well as their action potentials (MUAPs). Here, we present a modification of this original model in order to achieve a real-time performance of the algorithm as well as a parallel computation implementation of the algorithm on Graphics Processing Unit (GPU).

Recursive Decomposition of Electromyographic Signals With a Varying Number of Active Sources: Bayesian Modeling and Filtering.

Objective: This paper describes a sequential decomposition algorithm for single-channel intramuscular electromyography (iEMG) generated by a varying number of active motor neurons.

Methods: As in previous work, we establish a hidden Markov model of iEMG, in which each motor neuron spike train is modeled as a renewal process with inter-spike intervals following a discrete Weibull law and motor unit action potentials are modeled as impulse responses of linear time-invariant systems with known prior. We then expand this model by introducing an activation vector associated with the state vector of the hidden Markov model.