A Biomimetic Approach Towards a Dexterous Neuroprosthesis

项目总结 颈髓损伤导致手臂和手功能丧失，这显著限制了独立性 并导致在人的一生中产生成本。脑机接口(BCI)可以用来绕过 用于控制机械臂并提供体感反馈的损伤组织。两个主要限制 目前用于手臂和手控制的最先进的BCI是：(1)无法控制由 假手和(2)缺乏来自手的体感反馈。在拟议的研究中，我们寻求 通过实施解码策略来显著改进假肢的灵巧控制，从而使 用户不仅可以控制手臂和手的运动，还可以控制通过手传递的力。 我们预计，我们的仿生解码方法将产生对假肢的直观、灵活的控制 手。触觉感觉将通过皮质内微刺激(ICMS)传递给用户 躯体感觉皮质。刺激的时空模式将基于我们的基本科学 了解触觉信息在躯体感觉皮质中是如何编码的，我们预计这将导致更多 自然而直观的感觉。为了实现我们开发灵巧神经假体的目标，我们已经 将来自匹兹堡大学的具有人类BCI经验的团队与基础 在皮特和芝加哥大学都有科学专长。我们将与植入物领域的专家合作 神经技术(黑石微系统)和机器人(生物机器人研究所)，以确保设备 硬件允许我们采取仿生的方法进行控制和反馈，着眼于临床翻译。 共有4名参与者将在一项多地点研究中接受测试，以实现以下三个具体目标。目标1： 通过体感皮质的ICMS唤起自然和直观的触觉感觉。我们希望这种仿生技术 ICM将唤起与日常触觉更相似的感觉，并直观地传达 与标准的固定频率ICMS相比，ICMS可以提供更多关于接触对象的信息。目标2：导出运动学和 来自运动皮质的运动信号，用于手动控制。我们将评估运动皮质神经元的程度 对施加在物体上的力进行编码。基于这些观察，我们将开发混合解码器，使其能够 使用基于协同的方法控制运动和力。目标3：展示改进的手臂 以及具有仿生感觉运动BCI的手功能，该BCI结合了在Aim 1中开发的感觉反馈 随着目标2中开发的混合解码。将使用一组功能评估，包括新颖的 专门为感觉运动假体设计的指标，以及NIH确定的成熟测试 公共数据元素。我们预计受试者将使用仿生技术显著提高他们的灵巧度。 BCI与非仿生BCI或没有体感反馈的BCI相比。

PROJECT SUMMARY Cervical spinal cord injury results in the loss of arm and hand function, which significantly limits independence and results in costs over the person’s lifespan. A brain-computer interface (BCI) can be used to bypass the injured tissue to enable control of a robotic arm and to provide somatosensory feedback. Two primary limitations of current state-of-the-art BCIs for arm and hand control are: (1) the inability to control the forces exerted by the prosthetic hand and (2) the lack of somatosensory feedback from the hand. In the proposed study, we seek to considerably improve dexterous control of prosthetic limbs by implementing decoding strategies that enable the user to not only control the movements of the arm and hand, but also the forces transmitted through the hand. We anticipate that our biomimetic approach to decoding will yield intuitive, dexterous control of the prosthetic hand. Tactile sensations will be conveyed to the user through intracortical microstimulation (ICMS) of somatosensory cortex. The spatiotemporal patterns of stimulation will be based on our basic scientific understanding of how tactile information is encoded in somatosensory cortex, which we expect will result in more natural and intuitive sensations. In order to achieve our goal of developing a dexterous neuroprosthesis, we have brought together a team with human BCI experience from the University of Pittsburgh along with the basic science expertise at both Pitt and the University of Chicago. We will collaborate with experts in implantable neurotechnology (Blackrock Microsystems) and robotics (The Biorobotics Institute) to ensure that the device hardware allows us to take a biomimetic approach for control and feedback with an eye toward clinical translation. A total of 4 participants will be tested in a multisite study to accomplish the following three specific aims. Aim 1: Evoke natural and intuitive tactile sensations through ICMS of somatosensory cortex. We expect that biomimetic ICMS will evoke sensations that more closely resemble everyday tactile sensations and intuitively convey information about contacted objects than does standard fixed-frequency ICMS. Aim 2: Derive kinematic and kinetic signals from motor cortex for hand control. We will assess the degree to which motor cortical neurons encode forces exerted on objects. Based on these observations, we will develop hybrid decoders that enable controlling both the movement and force using a synergy-based approach. Aim 3: Demonstrate improved arm and hand function with a biomimetic sensorimotor BCI that combines the sensory feedback developed in Aim 1 with the hybrid decoding developed in Aim 2. A battery of functional assessments will be used including novel metrics designed specifically for sensorimotor prosthetics along with well-established tests identified in the NIH Common Data Elements. We anticipate that subjects will substantially improve their dexterity using a biomimetic BCI as compared to non-biomimetic BCIs or BCIs without somatosensory feedback.