A Biomimetic Approach Towards a Dexterous Neuroprosthesis

项目摘要 颈脊髓损伤导致手臂和手功能的丧失，这显著限制了独立性 并在人的整个生命周期中产生成本。脑机接口（BCI）可用于绕过 受伤的组织，以使机器人手臂的控制和提供体感反馈。两个主要限制 当前用于手臂和手控制的最先进的BCI的缺点是：（1）不能控制由手臂和手施加的力。 假手和（2）缺乏来自手的体感反馈。在拟议的研究中，我们力求 通过实施解码策略，大大改善了假肢的灵巧控制， 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直观，灵巧的假肢控制 手触觉将通过皮层内微刺激（ICMS）传达给用户， 躯体感觉皮层刺激的时空模式将基于我们的基础科学 了解触觉信息是如何在体感皮层编码的，我们预计这将导致更多的 自然和直觉的感觉。为了实现我们开发灵巧神经假体的目标，我们 我从匹兹堡大学沿着召集了一个具有人类脑机接口经验的团队， 皮特和芝加哥大学的科学专家。我们将与植入式医疗专家合作， 神经技术（黑石微系统公司）和机器人技术（生物机器人研究所），以确保该设备 硬件使我们能够采取仿生方法进行控制和反馈，并着眼于临床转化。 将在多中心研究中对总共4名受试者进行测试，以实现以下三个特定目标。目标1： 通过躯体感觉皮层的ICMS唤起自然和直观的触觉。我们希望仿生 ICMS将唤起更接近日常触觉的感觉，并直观地传达 与标准固定频率ICMS相比，它可以提供有关接触对象的信息。目标2：推导运动学和 来自运动皮层的运动信号用于手部控制。我们将评估运动皮层神经元 编码施加在物体上的力。基于这些观察，我们将开发混合解码器， 使用基于协同的方法来控制运动和力。目标3：展示改善的手臂 和手部功能与仿生感觉运动BCI相结合的感觉反馈，在目标1 在Aim 2中开发的混合解码。将使用一系列功能评估，包括新的 专门为感觉运动假肢设计的指标，沿着NIH确定的成熟测试 通用数据元素。我们预计，受试者将大大提高他们的灵活性， BCI与非仿生BCI或无体感反馈的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.