  Real-time classification of Electromyography signals is the most challenging
part of controlling a prosthetic hand. Achieving a high classification accuracy
of EMG signals in a short delay time is still challenging. Recurrent neural
networks (RNNs) are artificial neural network architectures that are
appropriate for sequential data such as EMG. In this paper, after extracting
features from a hybrid time-frequency domain (discrete Wavelet transform), we
utilize a set of recurrent neural network-based architectures to increase the
classification accuracy and reduce the prediction delay time. The performances
of these architectures are compared and in general outperform other
state-of-the-art methods by achieving 96% classification accuracy in 600 msec.
