An Open Source Simulator for Multi Degree-Of-Freedom Brain-Machine Interfaces

多自由度脑机接口的开源模拟器

• 批准号: 10616502
• 负责人: Jonathan Chau-Yan Kao
• 金额: $ 38.08万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10616502
• 关键词: Acceleration; Algorithms; Area; Artificial Intelligence; Behavior; Brain; Brain region; Clinical; Clinical Trials; Communication; Communities; Computer software; Data; Democracy; Development; Dimensions; Emerging Technologies; Equation; Evaluation; Feedback; Freedom; Future; Goals; Human; Learning; Macaca; Modeling; Motor; Motor Activity; Motor Cortex; Movement; Movement Disorders; Neural Network Simulation; Neurosciences; Paralysed; Participant; Performance; Persons; Policies; Population; Prosthesis; Psychological reinforcement; Publishing; Reporting; Research; Research Personnel; Robotics; Signal Transduction; Software Tools; System; Techniques; Technology; TensorFlow; Testing; Time; Training; Translating; Work; arm; brain machine interface; deep learning; deep reinforcement learning; design; experimental study; human model; human-in-the-loop; improved; innovation; kinematics; mind control; neural; neurosurgery; nonhuman primate; novel; open source; recurrent neural network; response; simulation; tool

PROJECT SUMMARY For millions with movement disorders including paralysis and ALS, intracortical brain-machine interfaces (BMIs) are an emerging technology that aims to restore lost motor function and communication. The main component of a BMI is a decoder algorithm that translates neural activity from motor areas of the brain into the kinematics of a prosthetic device. Due to the complexity of these systems, which includes the BMI user interacting with the decoded kinematics in a closed-feedback loop, current technology requires expensive and invasive experiments to design, optimize, and validate decoder algorithms. The need for such experiments (1) results in slow develop- ment and evaluation of decoder algorithms, and (2) limits the scope of people who can work on these problems to a small group of nonhuman primate and clinical trial labs. As a consequence, BMIs have remained in pilot clinical trials since their first reported demonstration in 2004. We propose a new open-source simulator for multiple degree-of-freedom (DOF) BMI systems. The goals of this simulator are to (1) reduce the time it takes to evaluate and optimize BMI algorithms from months to minutes, and (2) significantly expand the community of researchers who develop testable algorithms for BMIs. To build the simulator, we propose neural encoding models that generate synthetic motor cortical activity for multiple DOF tasks. This is possible because neural population activity is relatively low-dimensional and has dynamics, which can be learned via recurrent neural networks (RNNs). We build our neural simulators using data collected from human clinical trials during point-to-point multi-DOF reaches. We also propose to develop new models of human controllers. This solves an important problem in BMIs: users learn new control strategies when controlling a particular BMI decoder algorithm. Our simulator uses deep imitation and reinforcement learning to solve this problem. It is constrained through imitation learning to perform actions like a human. It is optimized through reinforcement learning to explore new strategies – under the constraint of being human-like – to optimally control the BMI. Together, we expect these innovations will result in a purely software simulator that accurately predicts BMI performance and enables design and optimization. This tool will be open-sourced and available to all, enabling widespread development of BMIs.

项目摘要 对于数百万患有运动障碍（包括瘫痪和ALS）的人来说，皮质内脑机接口（BMI） 是一项旨在恢复失去的运动功能和交流的新兴技术。主要成分 BMI是一种解码器算法，它将大脑运动区域的神经活动转换为运动学 一个假肢装置。由于这些系统的复杂性，包括BMI用户与 在封闭反馈回路中解码运动学，当前的技术需要昂贵和侵入性的实验 设计、优化和验证解码器算法。这种实验的需要（1）导致缓慢的发展- 解码器算法的评估和评价，以及（2）限制了可以研究这些问题的人员的范围 一小群非人类灵长类动物和临床试验实验室。因此，BMI仍处于试点阶段 自2004年首次报告演示以来， 我们提出了一个新的开源模拟器多自由度（DOF）BMI系统。这个的目标 模拟器的目的是（1）将评估和优化BMI算法所需的时间从数月减少到数分钟， （2）显著扩大为BMI开发可测试算法的研究人员群体。建立 模拟器，我们提出了神经编码模型，产生多自由度的合成运动皮层活动 任务这是可能的，因为神经群体活动是相对低维的，并且具有动态性， 可以通过递归神经网络（RNN）学习。我们使用从以下来源收集的数据来构建神经模拟器： 点对点多自由度范围内的人体临床试验。我们还建议开发新的人类模型， 控制器。这解决了BMI中的一个重要问题：用户在控制 BMI解码算法。我们的模拟器使用深度模仿和强化学习来解决这个问题 问题.它通过模仿学习来限制自己像人类一样执行动作。它通过以下方式进行优化： 强化学习探索新的策略-在像人类一样的约束下-以最佳控制 BMI。总之，我们希望这些创新将导致一个纯粹的软件模拟器，准确地预测 BMI性能，并实现设计和优化。该工具将是开源的，所有人都可以使用， 使BMI得以广泛发展。