We present a methodology to predict the presence of myopathy (muscle disease) from intramuscular electromyography (EMG) signals. By evaluating the shape and frequency of electrical action potentials produced by muscular fibers and captured in EMG measurements, a physician can often detect both the presence and the severity of such disorders. However, EMG measurements can vary significantly acro...

Handwriting--one of the most important developments in human culture--is also a methodological tool in several scientific disciplines, most importantly handwriting recognition methods, graphology and medical diagnostics. Previous studies have relied largely on the analyses of handwritten traces or kinematic analysis of handwriting; whereas electromyographic (EMG) signals associated with handwri...

In the brain computer interface (BCI) research field, a neurological occurrence is the only source of control in any BCI system. Artifacts are contrary signals that can affect with neurological phenomena. These artifacts may lead to the source of control in BCI systems. Electromyography (EMG) and electrooculography (EOG) artifacts are physiological artifacts that are carefully processed in BCI ...

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...

In spite of the great advances in the mechanical and electronic components of prosthetic hands, they still lack the high number of degrees of freedom present in the real human hand. That is due, not to technical deficiencies, but to the much reduced amount of independent control signals available when using surface electromyography (s-EMG) from the forearm stump or other artificial sensors. Cro...

A temporal analysis of electromyographic (EMG) activity has widely been used for non-invasive study of muscle activation patterns. Such an analysis requires robust methods to accurately detect EMG onset. We examined whether data conditioning, supplemented with Teager-Kaiser Energy Operator (TKEO), would improve accuracy of the EMG burst onset detection. EMG signals from vastus lateralis, collec...

In this study we propose an automatic method for solving convolutive mixtures separation. The independent components are extracted by frequency domain analysis, where the convolutive model can be solved by instantaneous mixing model approach. The signals are reconstructed back in the observation space resolving the ICA model ambiguities. Simulations are carried out to test the validity of the p...

Electromyography computed tomography (EMG-CT) method is proposed for visualizing the individual muscle activities in the human forearm. An EMG conduction model was formulated for reverse-estimation of muscle activities using EMG signals obtained with multi surface electrodes. The optimization process was calculated using sequential quadratic programming by comparing the estimated EMG values fro...

Background: Muscle fatigue is an important issue in neuromuscular rehabilitation. Better control of this phenomenon would result in better prevention of its consequent physiological damages.Objective: To provide a mathematical representation of muscle fatigue as a function of time.Methods: We conducted this study by combining the EMG-based estimation methods of muscle activation with the availa...