Use of electromyographic signals for characterization of voluntary coughing in humans with and without spinal cord injury-A systematic review.

Introduction: Individuals with spinal cord injury (SCI) frequently have an ineffective cough ability due to dysfunctions in expiratory muscles. In such cases, several articles have reported the occurrence of residual muscular activity in muscles that are accessory to coughing. The knowledge about this activity may be useful for building cough assistance devices.

Spatially distributed surface electromyography signal simulator.

Electromyographic signals are of great importance to current biomedical research society since they may be used in several ways as, for example, in the diagnosis of neuromuscular diseases, the control of active prosthetic limbs as well as the test and validation of medical equipment.

Motor unit action potential conduction velocity estimated from surface electromyographic signals using image processing techniques.

In surface electromyography (surface EMG, or S-EMG), conduction velocity (CV) refers to the velocity at which the motor unit action potentials (MUAPs) propagate along the muscle fibers, during contractions. The CV is related to the type and diameter of the muscle fibers, ion concentration, pH, and firing rate of the motor units (MUs). The CV can be used in the evaluation of contractile properties of MUs, and of muscle fatigue.

An experimental evaluation of the incidence of fitness-function/search-algorithm combinations on the classification performance of myoelectric control systems with iPCA tuning.

Background: The information of electromyographic signals can be used by Myoelectric Control Systems (MCSs) to actuate prostheses. These devices allow the performing of movements that cannot be carried out by persons with amputated limbs. The state of the art in the development of MCSs is based on the use of individual principal component analysis (iPCA) as a stage of pre-processing of the classifiers.

Evaluating different combinations of feature selection algorithms and cost functions applied to iPCA tuning in myoelectric control systems.

A myoelectric control system extracts information from electromyographic (EMG) signals and uses it to control different types of prostheses, so that people who suffered traumatisms, paralysis or amputations can use them to execute common movements. Recent research shows that the addition of a tuning stage, using the individual component analysis (iPCA), results in improved classification performance. We propose and evaluate a set of novel configurations for the iPCA tuning, based on a biologically inspired optimization procedure, the artificial bee colony algorithm.