EAGER: Bidirectional Body-Brain-Machine Interface (B3MI) for Control of Complex Dynamics

这个Mind，Machine，and Motor Nexus（M3 X）早期概念探索性研究（EAGER）项目为植入式双向脑机接口提出了一个新的愿景。双向脑机接口从大脑读取信息并向大脑写入信息。这些技术有可能帮助恢复神经运动损伤后的功能，通过补充内在的感觉和运动通路与可用于控制辅助设备的工程通路。然而，成功应用该技术需要用户接受大量培训，以学习如何使用界面来控制辅助设备。该项目将通过推进该项目的总体目标，即理解和塑造大脑如何使用双向脑机接口来控制具有复杂动力学的物理机器，来促进科学进步和促进国民健康。该项目的具体目标是：1）描述大脑如何学习联合收割机结合内在感觉输入（视觉和体感），沿着工程感觉输入和运动输出，以控制具有复杂动力学的新型设备; 2）测试构建高性能双向接口的新方法，这些接口可以相互适应，以增强用户在环控制。该项目团队将使用临床相关模型测试双向身体和大脑接口，该模型允许对复杂的学习动力学进行科学严谨的研究。该研究有望对辅助设备和康复治疗的未来发展产生影响，其中设计和优化用户在环系统的方法将能够提高设备的性能和定制，以满足用户不断变化的需求和能力。该项目还通过研究指导支持研究生教育，其长期目标是开发新的知识和工程工具，用于优化用户在环辅助设备。当用户从设备接收反馈并使用该反馈实时改变设备的性能时，用户成为设备控制回路的一部分。目前的脑机接口是使用统计学和机器学习的方法设计的，这些方法不适合用户处于循环中时创建的闭环、自适应、动态环境。作为优化多通路感觉运动接口的第一步，该研究旨在：（1）发现当用户学习在双向身体和大脑-机器接口（B3 MI）中控制复杂动力学时，感觉和运动通路是如何整合的;（2）应用这些知识来合成和测试双向接口，优化具有复杂动力学的机器的用户在环控制。该项目使用临床相关的非人类灵长类动物（NHP）模型，以人类受试者实验无法实现的方式促进对复杂学习动力学的严格研究。这项研究有两个目的。第一个旨在凭经验测量感官运动变换对应于不同的路径，通过配对视觉或神经感官输入与手动或神经运动输出作为NHP控制接口与不同的机器动力学（第一和第二阶）。第二个目标是综合B3 MI以优化闭环系统性能，并在控制物理机器动态的同时测试性能。该研究在NHP中使用高时空分辨率，侵入性神经记录和刺激技术来创建新型闭环双向B3 MIs。这项研究将使用一种新的自动跟踪任务，其中测量的输入和输出信号的频谱分析被用来直接量化感觉运动变换。接口将使用鲁棒控制理论的既定技术进行合成。将使用既定试验任务的性能指标评估界面性能，并使用人类运动控制的既定指标量化感觉运动转换。这项工作承诺的科学和工程的进步，将提高的鲁棒性和实用性的双向神经接口的辅助设备和康复application.This奖项反映了NSF的法定使命，并已被认为是值得的支持，通过评估使用基金会的智力价值和更广泛的影响审查标准。

This Mind, Machine, and Motor Nexus (M3X) EArly-concept Grant for Exploratory Research (EAGER) project advances a novel vision for implantable bidirectional brain-machine interfaces. Bidirectional brain-machine interfaces read and write information from and to the brain. These technologies have potential to help restore function after neuromotor injury by supplementing intrinsic sensory and motor pathways with engineered pathways that can be used to control assistive devices. However, successful application of the technology requires users to undergo substantial training to learn how to use the interface to control the assistive device. This project will promote the progress of science and advance the national health by advancing the project's overarching goal, which is to understand and shape how the brain can use a bidirectional brain-machine interface to control physical machines with complex dynamics. The specific objectives of the project are: 1) to characterize how the brain learns to combine intrinsic sensory inputs (vision and somatosensation), along with engineered sensory inputs and motor outputs, to control novel devices with complex dynamics; and 2) to test new ways to build high-performance bidirectional interfaces that can co-adapt to enhance user-in-the-loop control. The project team will test bidirectional body and brain interfaces with foundational research using a clinically relevant model that allows the scientifically rigorous study of complex learning dynamics. The research promises to be impactful in the future development of assistive devices and rehabilitation therapies, where methods to design and optimize user-in-the-loop systems will enable improved performance and customization of devices to users' evolving needs and capabilities. The project also supports graduate education through research mentorship.The long-term goal of this work is to develop new knowledge and engineering tools that can be used to optimize user-in-the-loop assistive devices. When a user receives feedback from a device and uses that feedback to alter the device's performance in real-time, the user becomes part of the device control loop. Current brain-machine interfaces are designed using methods from statistics and machine learning that are ill-suited to the closed-loop, co-adaptive, dynamic environments created when the user is in the loop. As a first step towards optimizing multi-pathway sensorimotor interfaces, the research seeks: (1) to discover how sensory-and-motor pathways are integrated as a user learns to control complex dynamics in a bidirectional body-and-brain-machine interface (B3MI); and (2) to apply this knowledge to synthesize and test a bidirectional interface that optimizes user-in-the-loop control of a machine with complex dynamics. The project uses using a clinically relevant non-human primate (NHP) model that facilitates the rigorous study of complex learning dynamics in a way that is impracticable through human subject experimentation. The research has two aims. The first seeks to empirically measure sensorimotor transforms corresponding to different pathways obtained by pairing visual or neural sensory input with manual or neural motor output as a NHP controls interfaces with different machine dynamics (1st and 2nd order). The second seeks to synthesize B3MIs to optimize closed-loop system performance, and to test performance while controlling physical machine dynamics. The research uses high spatiotemporal resolution, invasive neural recording and stimulation techniques in a NHP to create novel closed-loop bidirectional B3MIs. The study will use a novel trajectory-tracking task wherein spectral analysis of measured input and output signals are used to directly quantify sensorimotor transforms. Interfaces will be synthesized using established techniques from robust control theory. Interface performance will be assessed using performance metrics on established assay tasks, and sensorimotor transformations will be quantified using established metrics from human motor control. This work promises scientific and engineering advances that will improve the robustness and utility of bidirectional neural interfaces for assistive device and rehabilitation applications.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.