Non-Invasive Models of Human Brain-Computer-Interface Control of Robots

在许多情况下，熟练的人类操作员必须实时操作大量控制变量来指导机器人设备的灵巧运动。这些包括控制挖掘机，遥控手术机器人，以及操作尖端的脑控假肢。然而，目前还不清楚如何组织大量的控制变量集，以优化人们学习控制复杂机器的方式。该项目旨在通过解决与自适应脑机接口的设计和实现有关的两个问题来促进科学进步和促进国民健康，例如严重受损的人用来控制辅助机器人的接口。该研究方法的一个重要创新是非侵入性地记录手指运动，作为通常由皮质内脑机接口(IBCI)提供的高维输入的替代。本项目解决的具体研究问题包括：1)控制信号应该如何在控制界面上呈现给用户，以优化机器的输出行为？以及2)机器人系统是否应该预测用户希望它做什么并相应地调整其行为，如果是这样的话，应该如何在用户和机器之间共享任务级控制以优化任务性能？项目成果有望适用于一系列困难的人机交互问题。获奖者的机构是一家拉美裔服务机构；这项研究包括专门接触代表性不足的群体、本科生和当地社区的外联活动。该项目将使用两种皮质内脑机接口(IBCI)模型来评估如何将高维人类输入映射到6自由度具体化机械臂的指挥变量上。该项目使用手指运动的非侵入性记录作为iBCI通常提供的高维输入的替代。第一个模型将手指关节线性地投影到七个不同的机器人命令空间(效应器位置、关节速度、马达扭矩等)中的一个。该项目团队将评估人类受试者使用辅助机器人在日常生活任务(例如，移动桌面上的物体或将杯子送到嘴边)的七个不同界面中的每一个的表现。通过这样做，他们将确定命令空间编码在人类学习速度和任务熟练程度的最终程度中所起的作用。第二个模型获取人类的运动学输入，以驱动运动皮质神经元的深层神经网络模型，然后通过解码算法传递其放电率，以推断机器人的命令；这是一个明确且经过验证的皮质内脑机接口模型。项目团队将使用该模型来确定在线解码器对模拟神经输入的最佳适配率，以及在优化辅助机器人机器的任务性能时适配率与命令空间选择的交互程度。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

There are many situations where a skilled human operator must manipulate a large number of control variables in real-time to direct the dexterous motion of a robotic device. These include controlling an excavator, teleoperating a surgical robot, and operating a cutting-edge brain-controlled prosthetic limb. However, it remains unknown how large sets of control variables can be organized to optimize how people learn to control complex machines. This project seeks to promote the progress of science and advance the national health by addressing two questions related to the design and implementation of adaptive brain-machine-interfaces such as those used by severely impaired people to control assistive robotics. An important novelty of the researched approach is the non-invasive recording of finger motions as a proxy for the high-dimensional inputs typically provided by intracortical brain-computer interfaces (iBCI). The specific research questions addressed by this project include: 1) "How should control signals be presented to the user at the control interface to optimize output behavior of the machine?", and 2) "Should the robotic system predict what the user wants it to do and adapt its behavior accordingly, and if so, how should task-level control be shared between the user and the machine to optimize task performance?". Project outcomes promise to be applicable to a wide range of difficult human-machine interaction problems. The awardee's institution is a Hispanic Serving Institution; the research includes outreach activities that specifically engage underrepresented groups, undergraduate students, and the local community.The project will use two models of intracortical brain-computer interfaces (iBCI) to evaluate how high-dimensional human input should be mapped onto command variables for a 6 degree-of-freedom embodied robotic arm. The project uses non-invasive recording of finger motions as a proxy for the high-dimensional inputs typically provided by iBCIs. The first model linearly projects finger articulations into one of seven different robot command spaces (effector position, joint velocity, motor torques, etc.). The project team will evaluate how human subjects perform with the assistive robot on tasks of daily living (e.g., moving objects on a tabletop or bringing a cup to their mouth) with each of the seven different interfaces. By doing so, they will determine the role that command space encoding plays in the rate of human learning and the ultimate extent of task proficiency. The second model acquires human kinematic input to drive a deep neural network model of motor cortex neurons, whose firing rates are then passed through a decoding algorithm to infer commands for the robot; this is an explicit and validated model of intracortical brain-computer interfaces. The project team will use this model to determine optimal rates of online decoder adaptation to emulated neural input, and the extent to which the adaptation rates interact with the choice of command space in optimizing task performance of the assistive robotic machine.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.