The era of artificial neural network (ANN) began with a simplified application in many fields
and remarkable success in pattern recognition (PR) even in manufacturing industries …

The development of an advanced human–machine interface has always been an interesting
research topic in the field of rehabilitation, in which biomedical signals, such as myoelectric …

Considerable scientific and technological efforts have been devoted to develop
neuroprostheses and hybrid bionic systems that link the human nervous system with …

The classification of surface electromyography (sEMG) signal has an important usage in the
man-machine interfaces for proper controlling of prosthetic devices with multiple degrees of …

This report describes an early version of a technique for decomposing surface
electromyographic (sEMG) signals into the constituent motor unit (MU) action potential …

The purpose of this research is to select the best features to have a high rate of motion
classification for controlling an artificial hand. Here, 19 EMG signal features have been taken …

Surface electromyogram (sEMG) signals can be applied in medical, rehabilitation, robotic,
and industrial fields. As a typical application, a myoelectric prosthetic hand is controlled by …

Effective electromyographic (EMG) signal characterization is critical in the diagnosis of
neuromuscular disorders. Machine-learning based pattern classification algorithms are …

Surface Electromyography (EMG)-based pattern recognition methods have been
investigated over the past years as a means of controlling upper limb prostheses. Despite …

Electromyographic (EMG) signals are composed of the superposition of the activity of
individual motor units. Techniques exist for the decomposition of an EMG signal into its …