Electromyography (EMG) signals that represent the electrical activity of muscles are increasingly used for various clinical and biomedical applications. Detection, processing and classification of EMG signals require advanced soft computing methods to gain better understanding and precise evaluation of these signals in various applications such as rehabilitation technology, neurophysiological disorders and assistive technological findings. This paper provides comprehensive overview of the different methods for classification of EMG signals.
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