Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:736-739. doi: 10.1109/EMBC48229.2022.9871444.
Traditional methods for the development of a neuroprosthesis to perform closed-loop stimulation can be complex and the necessary technical knowledge and experience often present a high barrier for adoption. This paper takes a novel Model-Based Design approach to simplifying such closed-loop system development, and thereby lowering the adoption barrier. This work implements a computational model of different spike detection algorithms in Simulink® and compares their performances by taking advantage of synthetic neural signals to evaluate suitability for the intended embedded implementation. Clinical Relevance--- Closed-loop systems have been demonstrated to be suitable for brain repair strategies. Coupling two different brain areas by means of a neuroprosthesis can potentially lead to restoration of communication by inducing activity-dependent plasticity.
传统的开发神经假体进行闭环刺激的方法可能比较复杂，必要的技术知识和经验通常是采用的一个高门槛。本文采用一种新颖的基于模型的设计方法来简化这种闭环系统的开发，从而降低采用的门槛。这项工作在 Simulink®中实现了不同尖峰检测算法的计算模型，并利用合成神经信号来评估它们在预期的嵌入式实现中的适用性，从而比较它们的性能。临床相关性-闭环系统已被证明适用于脑修复策略。通过神经假体耦合两个不同的脑区，可以通过诱导活动依赖性可塑性来潜在地恢复通信。
