Collaborative Research: CDI Type I: Optimal and predictive control of neural prostheses using intracortical Brain Machine Interfaces

合作研究：CDI I 型：使用皮质内脑机接口对神经假体进行优化和预测控制

• 批准号: 0835554
• 负责人: Nitish Thakor
• 金额: $ 22.5万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0835554&HistoricalAwards=false
• 关键词: Collaborative; Research; CDI; Type; Optimal

The objective of this research is to develop an optimal predictive feedback control framework for neuroprostheses that are enabled by brain machine interfaces (BMIs). BMIs, broadly defined, are systems that interface the brain and a machine (computer) to sense neuronal activity and cognitive processes to restore impaired motor tasks in disabled subjects. The research focuses on: (1) development of a computational modeling framework for BMI-based neuroprostheses that can incorporate large data sets of intracortical neuronal measurements; (2) identification of natural and "surrogate" optimal feedback sensing paths in neuromotor prostheses; (3) development of computationally efficient predictive control algorithms that exploit models and feedback pathways in neuroprostheses; and (4) experimental evaluation through simulated neuroprosthetic multi-finger grasping.Current neuroprosthetic devices are largely "open-loop" and have limited performance due to their inability to incorporate multivariable feedback, sensory and interactive signals. This research addresses this limitation, striving to advance next-generation feedback-enabled neuroprosthetic devices. The proposed conceptual integration of optimal control theory with principles of neuroengineering and computational neuroscience provides a framework for analyzing the numerous feedback modalities intrinsically embedded in a complex interconnected system of neurons.For broader impacts, the research seeks to enable the transition of BMI-based neuroprostheses and assistive devices to stable extended use in human subjects suffering from peripheral neuropathies, spinal cord injuries, neuromuscular disorders, and amputations. The project will introduce a computational neuroscience paradigm in feedback control education while training biomedical engineers to directly employ tools from computational feedback control theory. A science outreach effort with Asa Packer Elementary School in Bethlehem and the middle school Summer Robotics Camp in Baltimore will motivate and excite young minds to think about solving technological "grand challenges" through demonstrations of brain functionality.

这项研究的目的是为脑机接口(BMI)实现的神经假体开发一种最优的预测反馈控制框架。广义的BMI是指连接大脑和机器(计算机)的系统，以感知神经元活动和认知过程，以恢复残疾受试者受损的运动任务。这项研究的重点是：(1)开发一个基于BMI的神经假体的计算建模框架，该框架可以包含皮质内神经元测量的大量数据集；(2)识别神经运动假体中自然的和“替代的”最优反馈感知路径；(3)开发计算高效的预测控制算法，利用神经假体中的模型和反馈路径；以及(4)通过模拟神经假体的多指抓取来进行实验评估。目前的神经假体设备基本上是“开环”的，由于它们不能整合多变量反馈、感觉和交互信号，因此性能有限。这项研究解决了这一局限，努力推进下一代反馈神经假体设备。最优控制理论与神经工程和计算神经科学原理的概念集成为分析复杂的神经元相互连接系统中内在嵌入的众多反馈模式提供了一个框架。从更广泛的影响来看，该研究试图使基于BMI的神经假体和辅助设备能够过渡到稳定的扩展使用，用于患有周围神经疾病、脊髓损伤、神经肌肉疾病和截肢的人类受试者。该项目将在反馈控制教育中引入计算神经科学范式，同时培训生物医学工程师直接使用计算反馈控制理论中的工具。与伯利恒的Asa Packer小学和巴尔的摩的中学暑期机器人夏令营一起开展的科学推广活动，将激励和激发年轻人思考通过展示大脑功能来解决技术“重大挑战”。