EMG signal classification using neural network with AR model coefficients

In this work authors proposes two approaches for discriminating between five predefined grasps using the EMG signals. In the first approach, the signal energy and the number of zero-crossing are used as signal features. In the second, the signal is modeled using the AR model and its coefficients became the features. Feature vectors created from both approaches are processed by a neural network with a linear transfer function, which classifies them as one of above mentioned grasps. From the experimental results, pattern identification using the AR model obtained good efficiency for the studied grasps.