A primate model of an intra-cortically controlled FES prosthesis for grasp

项目摘要 在不到十年的时间里，用于恢复运动的脑机接口（BCI）已经 从科幻小说发展到临床相关设备。然而，大多数现有的BCI，而 在技术上是显著的，需要用户连接到固定设备，并且只允许间歇控制 电脑光标或无实体的机器人肢体。他们要求将大脑活动与 恢复的运动要经常重新校准。我们已经开发出一种无线BCI， 一天，尽管猴子的手瘫痪了，但他们还是自愿恢复了运动，因为他们的大范围 正常的运动行为，如觅食，喂食或玩丰富的玩具。通过使用“自动编码 “我们将能够大大延长BCI在没有重新校准的情况下工作的时间。 我们已经开发了一种独特的模型脊髓损伤（SCI）使用长期植入输液泵 它将一种强效药物（河豚毒素）输送到放在手臂两个关键神经周围的袖口。这种药物会导致 一种神经阻滞，产生脊髓损伤的急性效应，无限期，但完全 停药后一天内恢复。在神经阻滞之前，我们将无线记录不仅是神经 来自大脑的信号，还有来自手臂和手部大量肌肉的肌电图（EMG）。 我们不仅会在实验室中进行典型的、受约束的运动行为， 完全不受约束的行为。我们将利用这些数据来开发 算法（“解码器”），其将神经信号转换为预测的EMG信号。发作后 瘫痪，我们的BCI将使用这些EMG预测作为功能性电刺激（FES）的控制信号， 使瘫痪的肌肉收缩，猴子可以通过电脑自动控制， 接口.我们将研究猴子学习使用FES BMI时逐渐变化的大脑活动。在 此外，我们将尝试通过开发“自适应”解码器来增强猴子的性能， 提高他们的表现与猴子自己的适应，以及“教师”解码器， 指导猴子，促使它们采取期望的控制策略，远离适得其反的策略。 这项技术使我们能够研究大脑在一系列运动中的表现。 以前从未有过的运动行为。在瘫痪期间，它可以让我们研究运动学习 以及自适应，而不受当前BCI的间歇可用性所强加的限制。最后给出 一个接近临床翻译所需的平台，我们将能够研究当前的限制。 解码器，并开发非线性和自适应解码器，旨在帮助猴子自己的自适应 流程.虽然本申请的重点是恢复抓握，但其一般原则将扩展到 控制伸展，下肢功能，甚至假肢。最终，这项工作将发展 接口、解码器和控制技术，这些都是将脑机接口从实验室转移到临床所必需的。

Project Summary In the space of barely over ten years, Brain Computer Interfaces (BCIs) used to restore movement have developed from the stuff of science fiction to clinically relevant devices. However, most existing BCIs, while technically remarkable, require the user to be wired to stationary equipment, and allow only intermittent control of a computer cursor or disembodied robotic limb. They require that the algorithm linking brain activity to the restored movement be frequently recalibrated. We have developed a wireless BCI that will operate 24 hours a day, restoring voluntarily movement to monkeys despite paralysis of their hand, for a broad range of their normal motor behaviors, such as foraging, feeding, or playing with enrichment toys. By using “autoencoding neural networks” we will be able to greatly extend the period over which the BCI will work without recalibration. We have developed a unique model of spinal cord injury (SCI) using a chronically implanted infusion pump that delivers a potent drug (tetrodotoxin) to cuffs placed around two key nerves in the arm. The drug causes a nerve block that produces the acute effects of spinal cord injury for indefinite periods of time, yet with full recovery within a day of stopping the drug. Prior to the nerve block, we will record wirelessly not only neural signals from the brain, but also electromyograms (EMGs) from a large number of muscles in the arm and hand. We will make these recordings not only during typical, constrained motor behaviors in the lab, but also during completely unconstrained behaviors while the monkey is in its home cage. We will use the data to develop algorithms (“decoders”) that transform the neural signals into predicted EMG signals. Following the onset of paralysis, our BCI will use these EMG predictions as control signals for Functional Electrical Stimulation (FES), causing contractions of the paralyzed muscles that the monkey can control voluntarily through the computer interface. We will study the gradually changing brain activity as the monkeys learn to use this FES BMI. In addition, we will attempt to augment the monkey's performance by developing “adaptive” decoders that improve their performance in parallel with the monkey's own adaptation, as well as “teacher” decoders that coach the monkeys, pushing them toward desired control strategies and away from counterproductive ones. This technology gives us the ability to study the brain's representation of movement across a range of motor behaviors that has never been possible before. During paralysis, it will allow us to study motor learning and adaption without the limitations imposed by the intermittent availability of current BCIs. Finally, it provides a platform close to that necessary for clinical translation, with which we will be able to study the limits of current decoders and to develop nonlinear and adaptive decoders designed to assist the monkey's own adaptive processes. While this application is focused on restoration of grasp, its general principles will extend to the control of reaching, lower limb function, and even prosthetic limbs. Ultimately, this work will develop the interface, decoder, and control technology that will be necessary to move BCIs from the lab to the clinic.