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...

This paper introduces a novel and simple algorithm to extract the feature from Surface EMG signals recorded from the skin surface over forearm muscles. Surface EMG signal is decomposed into 16 frequency bands (FB) by wavelet packet transform (WPT), and then wavelet packet entropy (WPE) of every surface EMG signal is calculated by its relative wavelet energy in every FB. WPE is regarded as the f...

The electromyographic signals abbreviated as EMG, represent the amount of electrical potential generated by the muscle cells when they contract or when they are at rest. Basically, EMG signals can be classified into two types according to the place where they are extracted. The EMG signals detect from inside of the muscles are called as intramuscular EMG whereas EMG signals detect from skin sur...

Since the 1970s, electromyographic control of a prosthetic device has been attempted in a number of different ways. It was only until recently, that the classification of electromyographic signals was possible through the use of neural networks. With the advent of more advanced techniques like support vector machines this type of control is becoming more realistic. This would initiate a great s...

This study aims to quantitatively clarify the physiological features in rhythmically coordinated jaw and neck muscle EMG activities while chewing gum using EMG-EMG transfer function and EMG-EMG coherence function analyses in 20 healthy subjects. The chewing side masseter muscle EMG signal was used as the reference signal, while the other jaw (non-chewing side masseter muscle, bilateral anterior...