In this study, a new system composed of two modules (electromyostimulation + electromyography recording) is presented. It can analyze in real time EMG signals during electromyostimulation. In addition, we propose a new method based on wavelet decomposition to analyze changes in M-wave characteristics. It leads to introduce a new index related to muscular fatigue.

Decomposition of electromyogram (EMG) provides a valuable means of obtaining motor unit recruitment and firing rate information. The feasibility of decomposing surface EMG signals into their constituent motor unit action potential (MUAP) trains using independent component analysis (ICA) was examined using simulated EMG data. Surface EMG signals detected with an array of nine electrodes were sim...

The present growing field of molecular imaging, including multimodality microimaging techniques and spectroscopic approaches, is mainly based on small animal studies. Monitoring such models requires an efficient treatment and use of electrophysiological signals which may be spoiled by environmental effects especially when working with nuclear magnetic resonance (NMR) since radiofrequency (RF) p...

BACKGROUND Ensemble Empirical Mode Decomposition (EEMD) has been popularised for single-channel Electromyography (EMG) signal processing as it can effectively extract the temporal information of the EMG time series. However, few papers examine the temporal and spatial characteristics across multiple muscle groups in relation to multichannel EMG signals. EXPERIMENT The experimental data was ob...

To improve the quality of life for the disabled and elderly, this paper develops an upperlimb, EMG-based robot control system to provide natural, intuitive manipulation for robot arm motions. Considering the non-stationary and nonlinear characteristics of the Electromyography (EMG) signals, especially when multi-DOF movements are involved, an empirical mode decomposition method is introduced to...

The quantitative analysis of decomposed electromyographic (EMG) signals reveals information for diagnosing and characterizing neuromuscular disorders. Neuromuscular jitter is an important measure that reflects the stability of the operation of a neuromuscular junction. It is conventionally measured using single fiber electromyographic (SFEMG) techniques. SFEMG techniques require substantial phy...

Over the past 3 decades, various algorithms used to decompose the electromyographic (EMG) signal into its constituent motor unit action potentials (MUAPs) have been reported. All are limited to decomposing EMG signals from isometric contraction. In this report, we describe a successful approach to decomposing the surface EMG (sEMG) signal collected from cyclic (repeated concentric and eccentric...

A novel noise filtering algorithm based on ensemble empirical mode decomposition (EEMD) is proposed to remove artifacts in electrocardiogram (ECG) traces. Three noise patterns with different power--50 Hz, EMG, and base line wander--were embedded into simulated and real ECG signals. Traditional IIR filter, Wiener filter, empirical mode decomposition (EMD) and EEMD were used to compare filtering ...

Technologies for decomposing the electromyographic (EMG) signal into its constituent motor unit action potential trains have become more practical by the advent of a non-invasive methodology using surface EMG (sEMG) sensors placed on the skin above the muscle of interest (De Luca et al 2006 J. Neurophysiol. 96 1646-57 and Nawab et al 2010 Clin. Neurophysiol. 121 1602-15). This advancement has w...