The Electromyography (EMG) is a very important biomedical signal and can be used for verity of applications in clinical or biomedical field. This signal is used to detect abnormal muscle activities like impaired nourishment of an organ or part of body, inflammation of muscles, pinched nerves and peripheral nerve damages etc. The EMG signal is controlled by nervous system and is dependent on the...

Electromyography signal can be used for biomedical applications. It is complicated in interpretation, so it acquires advanced methods for detection, decomposition, processing, and classification. The techniques of EMG signal analysis such as: filtering, wavelet transform, and modeling will be presented in this paper to provide efficient and effective ways of understanding the signal. A comparis...

The surface electromyographic (EMG) signal is often contaminated by some degree of baseline noise. It is customary for scientists to subtract baseline noise from the measured EMG signal prior to further analyses based on the assumption that baseline noise adds linearly to the observed EMG signal. The stochastic nature of both the baseline and EMG signal, however, may invalidate this assumption....

background: electromyographic (emg) signal decomposition is the process by which an emg signal is decomposed into its constituent motor unit potential trains (mupts). a major step in emg decomposition is feature extraction in which each detected motor unit potential (mup) is represented by a feature vector. as with any other pattern recognition system, feature extraction has a significant impac...

Nowadays, analysis of electromyography (EMG) signal using wavelet transform is one of the most powerful signal processing tools. It is widely used in the EMG recognition system. In this study, we have investigated usefulness of extraction of the EMG features from multiple-level wavelet decomposition of the EMG signal. Different levels of various mother wavelets were used to obtain the useful re...

Background: Electromyographic (EMG) signal decomposition is the process by which an EMG signal is decomposed into its constituent motor unit potential trains (MUPTs). A major step in EMG decomposition is feature extraction in which each detected motor unit potential (MUP) is represented by a feature vector. As with any other pattern recognition system, feature extraction has a significant impac...

Rectification of EMG signals is a common processing step used when performing electroencephalographic-electromyographic (EEG-EMG) coherence and EMG-EMG coherence. It is well known, however, that EMG rectification alters the power spectrum of the recorded EMG signal (interference EMG). The purpose of this study was to determine whether rectification of the EMG signal influences the capability of...