The rapid increase of wearable sensor devices poses new challenges for implementing continuous real-time processing of physiological data. Neuromorphic sensory-processing devices can enable both the measurement of bio-signals and their processing locally in compact embedded wearable systems. In particular, mixed-signal spiking neural networks implemented on neuromorphic processors can be integrated directly with the sensors to extract temporal data-streams in real-time with very low power consumption. In this work, we present a neuromorphic approach for classifying spatio-temporal data from electromyography (EMG) signals, which paves the way toward the realization of compact wearable solutions for neuroprosthetic control. Here we extend previously proposed delta-encoding methods to transform bio-signals into spike trains and use a spiking Recurrent Neural Network (SRNN) architecture to extract features from them. The SRNN was first simulated in software to find the optimal set of hyperparameters, and then validated on the neuromorphic hardware, with a difference in the performance of less than 2%. We describe how biologically plausible mechanisms such as Spike-Timing Dependent Plasticity (STDP) and soft Winner-Take-All (WTA) networks can be exploited to classify the EMG signals and show how their combined use in EMG data classification achieves competitive results with different datasets. Specifically, the classification performance for the Roshambo EMG dataset, which has three different classes, is above 85%, and for the basic finger movements dataset from the Ninapro database, which has eight different classes, reaches 55% accuracy.

中文翻译：

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使用混合信号神经形态处理系统的基于 EMG 的手势分类

可穿戴传感器设备的快速增长对实现生理数据的连续实时处理提出了新的挑战。神经形态感觉处理设备可以在紧凑的嵌入式可穿戴系统中实现生物信号的测量及其本地处理。特别是，在神经形态处理器上实现的混合信号尖峰神经网络可以直接与传感器集成，以非常低的功耗实时提取时间数据流。在这项工作中，我们提出了一种神经形态方法，用于对来自肌电图 (EMG) 信号的时空数据进行分类，这为实现用于神经假体控制的紧凑型可穿戴解决方案铺平了道路。在这里，我们扩展了先前提出的增量编码方法，将生物信号转换为尖峰序列，并使用尖峰循环神经网络 (SRNN) 架构从中提取特征。SRNN 首先在软件中模拟以找到最佳超参数集，然后在神经形态硬件上进行验证，性能差异小于 2%。我们描述了如何利用生物学上合理的机制，例如尖峰定时相关可塑性 (STDP) 和软赢家通吃 (WTA) 网络来对 EMG 信号进行分类，并展示它们在 EMG 数据分类中的组合使用如何在不同的情况下获得具有竞争力的结果。数据集。具体来说，具有三个不同类别的 Roshambo EMG 数据集的分类性能在 85% 以上，