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.

翻译：神经病变在临床中日益受到重视，因其可能永久危及患者生命。为支持患者康复，全植入式设备正成为最具前景的方案之一。然而，即便这类设备能成为复杂神经纳米网络系统的核心组成部分，仍面临诸多挑战。本文针对其中一项挑战——运动/感觉刺激的分类问题展开研究。通过探索四种不同类型的人工神经网络（ANNs），从大鼠坐骨神经电神经图（ENG）信号中提取多种感觉刺激。我们采用不同规模的数据集，通过对比各网络在准确率、F1分数和预测时间方面的性能，分析所研究ANNs用于实时分类的可行性。ANNs的设计基于将ENG信号建模为多输入多输出（MIMO）系统，以描述现有尖端植入式神经接口的测量特性。这些接口采用多触点袖带电极实现神经活动的纳米级空间分辨。本文的另一贡献在于提出了MIMO ENG信号模型。结果表明，某些ANNs更适用于实时应用，能够在$100$和$200$毫秒的信号窗口内实现超过$90\%$的准确率，且处理时间足够短，可有效支持病理康复。