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

项目摘要 在短短十多年的时间里，用于恢复运动的脑机接口(BCI)已经 从科幻小说的东西发展到临床相关的设备。然而，大多数现有的BCI，虽然 技术卓越，需要用户连接到固定设备，并且只允许间歇性控制 计算机光标或无实体的机器人肢体。他们要求将大脑活动与大脑活动联系起来的算法 恢复的动作需要经常重新校准。我们已经开发了一种无线脑机接口，它将在24小时内 一天，尽管猴子的手瘫痪了，但仍自愿恢复它们的活动，以获得广泛的 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” 神经网络“我们将能够大大延长BCI的工作周期，而不需要重新校准。 我们利用长期植入的输液泵建立了一种独特的脊髓损伤(SCI)模型。 这会将一种有效的药物(河豚毒素)输送到手臂上两条关键神经周围的手铐上。这种药物会引起一种 产生脊髓损伤的急性影响的神经阻滞，无限期，但完全 停药后一天内康复。在神经阻滞之前，我们不仅会无线记录神经 来自大脑的信号，也来自手臂和手的大量肌肉的肌电(EMG)。 我们将不仅在实验室中记录典型的受限运动行为，而且在 猴子在家中时完全不受约束的行为。我们将利用这些数据来开发 将神经信号转换成预测的肌电信号的算法(“解码器”)。在……开始之后 瘫痪，我们的脑机接口将使用这些肌电预测作为功能性电刺激(FES)的控制信号， 导致瘫痪肌肉的收缩，猴子可以通过计算机自愿控制 界面。我们将研究猴子学习使用这种FES BMI时大脑活动的逐渐变化。在……里面 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 在猴子自身适应的同时提高它们的性能，以及 训练猴子，把它们推向想要的控制策略，而不是适得其反的策略。 这项技术使我们能够研究大脑对一系列运动的表示 以前从来不可能发生的运动行为。在瘫痪期间，它将使我们能够研究运动学习 以及适应，而不受当前BCI间歇性可用带来的限制。最后，它提供了 一个接近临床翻译所需的平台，我们将能够利用它来研究当前 解码器和开发非线性和自适应解码器，旨在帮助猴子自己适应 流程。虽然这一应用程序的重点是恢复GRAPH，但其一般原则将扩展到 伸展能力、肢体功能、甚至假肢的控制。最终，这项工作将开发出 将BCI从实验室转移到临床所必需的接口、解码器和控制技术。

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.