removal of artifacts from bio-signals is a necessary step before automatic processing and obtaining clinical information. recently, many applications of empirical mode decomposition (emd) on biomedical researches have been presented that artifact reduction from bio-signals is one of them. emd separates a time series into finite numbers of its individual oscillations, which are called intrinsic ...

Electromyography (EMG) signals are muscles that enable the identification of human movements without need complex kinematics calculations. Researchers prefer EMG as input to control prosthetic arms and exoskeleton robots. However, proper algorithm classify from raw has been an interesting challenging topic researchers. Various studies have carried out produce EMG-based movement classification g...

Electromyographic (EMG) signal directly represents muscle activity. When recording EMG signals of closely located muscles on skin surface the problem of source separation arises, each acquired channel records signals coming from different muscles [1]. Working on extrinsic finger extensors EI (index) and EDM (little finger) provides a system with muscles that are closely located and independentl...

Electromyography (EMG) signals are becoming increasingly important in many applications, including clinical/biomedical, prosthesis or rehabilitation devices, human machine interactions, and more. However, noisy EMG signals are the major hurdles to be overcome in order to achieve improved performance in the above applications. Detection, processing and classification analysis in electromyography...

The analysis and classification of electromyography (EMG) signals are very important in order to detect some symptoms of diseases, prosthetic arm/leg control, and so on. In this study, an EMG signal was analyzed using bispectrum, which belongs to a family of higher-order spectra. An EMG signal is the electrical potential difference of muscle cells. The EMG signals used in the present study are ...