  Electromyography (EMG) is a way of measuring the bioelectric activities that
take place inside the muscles. EMG is usually performed to detect abnormalities
within the nerves or muscles of a target area. The recent developments in the
field of Machine Learning allow us to use EMG signals to teach machines the
complex properties of human movements. Modern machines are capable of detecting
numerous human activities and distinguishing among them solely based on the EMG
signals produced by those activities. However, success in accomplishing this
task mostly depends on the learning technique used by the machine to analyze
EMG signals; and even the latest algorithms do not result in flawless
classification. In this study, a novel classification method has been described
employing a multichannel Convolutional Neural Network (CNN) that interprets
surface EMG signals by the properties they exhibit in the power domain. The
proposed method was tested on a well-established EMG dataset, and the result
yields very high classification accuracy. This learning model will help
researchers to develop prosthetic arms capable of detecting various hand
gestures to mimic them afterwards.
