  Neuropathies are gaining higher relevance in clinical settings, as they risk
permanently jeopardizing a person's life. To support the recovery of patients,
the use of fully implanted devices is emerging as one of the most promising
solutions. However, these devices, even if becoming an integral part of a fully
complex neural nanonetwork system, pose numerous challenges. In this article,
we address one of them, which consists of the classification of motor/sensory
stimuli. The task is performed by exploring four different types of artificial
neural networks (ANNs) to extract various sensory stimuli from the
electroneurographic (ENG) signal measured in the sciatic nerve of rats.
Different sizes of the data sets are considered to analyze the feasibility of
the investigated ANNs for real-time classification through a comparison of
their performance in terms of accuracy, F1-score, and prediction time. The
design of the ANNs takes advantage of the modelling of the ENG signal as a
multiple-input multiple-output (MIMO) system to describe the measures taken by
state-of-the-art implanted nerve interfaces. These are based on the use of
multi-contact cuff electrodes to achieve nanoscale spatial discrimination of
the nerve activity. The MIMO ENG signal model is another contribution of this
paper. Our results show that some ANNs are more suitable for real-time
applications, being capable of achieving accuracies over $90\%$ for signal
windows of $100$ and $200\,$ms with a low enough processing time to be
effective for pathology recovery.
