The electrical signals produced by the muscles and nerves are analyzed to assess the state of neuromuscular function in subjects with suspected neuromuscular disorders. The repetitive activation of several individual motor units (MUs) results in a superposed pulse train and constitutes the electromyogram (EMG) signal. The analysis of the EMG is based on its basic constituent i.e. motor-unit act...

Decomposition of the electromyographic (EMG) signal into constituent action potentials and the identification of individual firing instances of each motor unit in the presence of ambient noise are inherently probabilistic processes, whether performed manually or with automated algorithms. Consequently, they are subject to errors. We set out to classify and reduce these errors by analyzing 1,061...

This paper describes an interactive computer program for decomposing EMG signals into their component motor-unit potential (MUP) trains and for averaging MUP waveforms. The program is able to handle single- or multi-channel signals recorded by needle or fine-wire electrodes during low and moderate levels of muscular contraction. It includes advanced algorithms for template matching, resolving s...

OBJECTIVE Automatic decomposition of surface electromyographic (sEMG) signals into their constituent motor unit action potential trains (MUAPTs). METHODS A small five-pin sensor provides four channels of sEMG signals that are in turn processed by an enhanced artificial intelligence algorithm evolved from a previous proof-of-principle. We tested the technology on sEMG signals from five muscles...

In this chapter we refer to the decomposition of the myoelectric (ME) signal as the procedure by which the ME signal is separated into its constituents motor units action potential trains (MUAPTs). This concept is illustrated in Figure 1. The development of a system to accomplish such a decomposition will be beneficial to both researchers interested in understanding motor unit properties and be...

This paper presents a developed tool capable of decomposing electromyographic signals (EMG) named EMG Decomposition. The main aim of this software is to extract and to visualize motor unit action potentials (MUAPs) extracted from EMG signals during a muscular contraction. The identification and clustering of MUAPs that show similar features is most important for the definition of such cluster s...

Wavelet analysis is often very effective because it provides a simple approach for dealing with local aspects of a signal. Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classi...

This paper presents a developed tool capable of decomposing electromyographic signals (EMG) named EMG Decomposition. The main aim of this software is to extract and to visualize motor unit action potentials extracted from EMG signals during a muscular contraction. The results obtained from the tool have application in the study of the behavior of the central nervous system and in the diagnosis ...

The observation that the activity of multiple muscles can be well approximated by a few linear synergies is viewed by some as a sign that such low-dimensional modules constitute a key component of the neural control system. Here, we argue that the usefulness of muscle synergies as a control principle should be evaluated in terms of errors produced not only in muscle space, but also in task spac...