The paper analyses the scope of multimodal parametric search (MPS) as the means for the decomposition of the electromyograms (EMGs). The approach is based on a predeened set of signal sources that are described by some parameters. These signal sources are searched for in a superimposed signal by calculation of the Euclidian distance and looking for its minimum. The signal sources are applied su...

OBJECTIVE This paper proposes a novel method for the extraction and classification of individual motor unit action potentials (MUAPs) from intramuscular electromyographic signals. METHODOLOGY The proposed method automatically detects the number of template MUAP clusters and classifies them into normal, neuropathic or myopathic. It consists of three steps: (i) preprocessing of electromyogram (...

In this study four pairs of Ag/AgCl surface electrodes respectively that pasted on the muscle belly of the biceps, triceps in the upper arm, palmaris longus and the brachioradialis muscle in the forearm are used to collect the electromyography (EMG) signals of six patterns of upper limb movements. The feature vectors of the EMG are extracted by wavelet decomposition method, and then these featu...

Abstract Electromyography (EMG) is the superposition of motor unit action potential (MUAP) in many muscle fibers time and space. In real measurement, EMG signals will contaminate signals, therefore they bring great difficulties to qualified analysis interpretation EEG it a momentous step remove artifacts from signals. recent years, new methods were developed for removal such as Multivariate Emp...

Empirical Mode Decomposition (EMD) is a data-driven and fully adaptive signal decomposition technique to decompose into its Intrinsic Functions (IMF). EMD has attained great attention due capabilities process in the frequency-time domain without altering frequency domain. EMD-based denoising techniques have shown potential denoise nonlinear nonstationary signals compromising signal’s characteri...

In the present paper we have described a system for acquiring, processing and decomposing EMG signals for the purpose of extracting as many motor unit action potential trains as possible with the greatest level of accuracy. This system consists of 4 main sections. The first section consists of methodologies for signal acquisition and quality verification. Three channels of EMG signals are acqui...

Blind source separation (BSS) algorithms, such as gradient convolution kernel compensation (gCKC), can efficiently and accurately decompose high-density surface electromyography (HD-sEMG) signals into constituent motor unit (MU) action potential trains. Once the matrix is blindly estimated on a signal interval, it also possible to apply same subsequent segments. Nonetheless, trained matrices ar...

Recently the use of high-density electromyogram (HD-EMG) signal acquisition setups has been promoted for myoelectric control and several databases have made open access scientific research. Despite this accelerated growth in literature, coupled with industry interest, some fundamental research questions are unanswered. For instance, can HD-EMG signals be decoded at smaller window sizes and, if ...

Trunk muscle electromyography (EMG) is often contaminated by the electrocardiogram (ECG), which hampers data analysis and potentially yields misinterpretations. We propose the use of independent component analysis (ICA) for removing ECG contamination and compared it with other procedures previously developed to decontaminate EMG. To mimic realistic contamination while having uncontaminated refe...

Important information gained using dynamic electromyography is to accurately define the muscle action and phase timing within the gait cycle. Human gait relies on selective timing and intensity of appropriate muscle activations for stability, loading and progression over the supporting foot during stance, and further to advance the limb in swing phase. A traditional clinical practice is to low ...