Pattern recognition of nerve signals using an artificial neural network

By using a microfabricated nerve chip with integrated electrodes through which peripheral nerves can generate and make electrical connects, it should be possible to control a remote prosthesis by processing the detected nerve signals. In this study different artificial neural networks have been employed for classification of such complex patterns of signals. The signals were obtained from four electrodes detecting muscle activity in a rat hindlimb as a consequence of applied stimulus to the rat right hindpaw. These signals recorded at four different sites resembles a situation of a nerve chip with four electrodes, which implies that we might be able to use the same strategy when analyzing data from a four-electrode chip to obtain information from the nervous system. In this paper we will address the usefulness of different network topologies for analyzing measured in-vivo data from an implanted perforated nerve chip.