CAREER: Closed Loop Modeling for Brain Machine Interface Design

I. Obeid0846351脑机接口是一种新兴技术，其目的是让截肢者和脊髓损伤患者使用来自大脑的信号控制假肢。拟议的工作将创造调查如何利用人类大脑的自然可塑性来创新更有效（从而更容易便携）的脑机接口仪器的方法。这将通过开发一种新的模拟器来实现，该模拟器同时模拟神经适应性，假肢和连接两者的脑机接口硬件。这个模拟器的一个关键要素将是能够使用实时视觉和本体感觉反馈的建模手臂训练虚拟脑细胞，从而随着时间的推移，提高精度与大脑可以控制假肢。拟议的工作将完成使用三个研究目标（1）设计和实现一个模拟平台，模拟自适应运动控制的人体肢体在三个空间。(2)设计和实现一个仪器测试平台，能够实现整个系列的脑机接口数据采集子系统，并系统地操纵它们的参数。 该系统将处理多达50 x 50个通道，并将收集性能统计数据，以量化数据通路中信息丢失或更改的方式和位置。(3)量化BMI性能如何预期响应于尖峰检测、尖峰分类和无线神经数据传输中的错误而降低。该项目将指导下一代脑机接口系统的开发，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，并为研究生阶段的神经工程教学开发新的实践教学材料。这项研究将推进皮层神经元功能集合建模技术，同时也为坦普尔大学及其他地区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.