CAREER: Closed Loop Modeling for Brain Machine Interface Design

I.Obeid0846351脑机接口是一项新兴技术，其目的是允许截肢者和脊髓损伤患者使用来自大脑的信号控制假肢。这项拟议的工作将创造一种手段，用于研究如何利用人脑的自然可塑性来创新更高效(从而更容易制造)的脑机接口仪器。这将通过开发一种新的模拟器来实现，该模拟器可以同时模拟完成任务时的神经适应，一个假肢，以及连接两者的脑机接口硬件。该模拟器的一个关键要素是能够使用来自建模手臂的实时视觉和本体感觉反馈来训练虚拟脑细胞，从而随着时间的推移，提高大脑控制假肢的精度。拟议的工作将通过三个研究目标来完成：(1)设计和实现一个模拟平台，在三维空间模拟人类肢体的自适应运动控制。(2)设计并实现了一套能够实现全系列脑机接口数据采集子系统并对其参数进行系统操作的仪器试验台。该系统将处理多达50x50个通道，并将收集性能统计数据，量化数据路径中信息丢失或更改的方式和位置。(3)量化在棘波检测、棘波分类和无线神经数据传输中的错误会如何降低BMI的性能。该项目将指导下一代脑机接口系统的发展，特别是植入式和无线系统，这些系统仍然是脑机接口成为现实治疗设备的障碍。该项目将为学生提供进行神经工程研究的培训，并为研究生水平的神经工程教学开发新的动手教学材料。这项研究将推进对大脑皮层神经元功能集合进行建模的技术，同时还将为坦普尔大学及其他地方的神经工程教育和研究创造教学工具。

I. Obeid0846351Brain Machine Interfaces are an emerging technology whose purpose is to allow amputees and spinal cord injury patients to control a prosthetic limb using signals derived from the brain. The proposed work will create the means for investigating how the natural plasticity of the human brain can be exploited to innovate more efficient (and thus more easily made portable) Brain Machine Interface instrumentation. This will be achieved through the development of a new simulator that simultaneously models neural adaptation in reaching tasks, a prosthetic limb, and Brain Machine Interface hardware that connects the two. A key element of this simulator will be the ability to use real-time visual and proprioceptive feedback from the modeled arm to train the virtual brain cells and thus, over time, improve the accuracy with which the brain can control the prosthesis.The proposed work will be accomplished using three Research Aims (1) Design and implement a simulation platform that models adaptive motor control of a human limb in three-space. (2) Design and implement an instrumentation testbed capable of realizing entire families of Brain Machine Interface data acquisition subsystems and systematically manipulating their parameters. The system will handle up to 50 x 50 channels and will collect performance statistics that quantify how and where information is lost or altered in the data pathway. (3) Quantify how BMI performance can be expected to degrade in response to errors in spike detection, spike sorting, and wireless neural data transmission.The project will guide the development of next generation Brain Machine Interface systems, especially the implantable and wireless systems that remain an obstacle to Brain Machine Interfaces becoming realistic therapeutic devices. The project will provide training to students to conduct neural engineering research and also for developing new hands-on instructional materials for teaching neural engineering at the graduate level. The research will advance techniques for modeling functional ensembles of cortical neurons while also creating pedagogical tools for conducting neural engineering education and research at Temple University and beyond.