Classification of Afferent Signals Recorded with a Single Cuff Electrode

The implementation of systems to restore sensorimotor functions in person with neurological disabilities is an important research area. In the past, many studies have been carried out to develop closed-loop neuroprostheses based on the processing of electroneurographic (ENG) signals recorded from physiological sensors using cuff electrodes. However, the potential of this approach is not completely clear. In this paper, an artificial neural network is used to discriminate afferent ENG signals evoked by different mechanical stimuli and recorded with a single cuff electrode. The preliminary results indicate that even single cuff ENG signals can be useful to extract interesting information with good performance. In the future, the possibility of discriminating additional stimuli using additional channels and more advanced classification techniques will be investigated.