Sensorized Neural Prosthetic

描述（由申请人提供）：该申请涉及广泛的挑战领域（06）使能技术和特定的挑战主题，06- hd -101改进假肢的接口，以提高康复效果。在过去的十年里，bmi的发展取得了相当大的进展。bmi是一种将大脑活动转化为对人工执行器（如假肢）的命令的设备。基于BMI技术的神经义肢旨在恢复脑损伤、神经系统疾病和肢体丧失患者的肢体活动能力。BMI研究中一个重要的未知问题是如何从神经义肢向使用者提供感觉反馈。这一领域的知识差距阻碍了临床相关神经义肢的发展，因为神经义肢系统努力复制的正常运动行为严重依赖于来自皮肤、肌肉和肌腱受体的感觉反馈。为了解决这个问题，我们建议开发一种感测神经假体，其中假手的触摸和压力传感器的信号作为初级体感皮层ICMS的时空模式传递给大脑。由于我们在猴子bmi模型方面的丰富经验，以及之前我们使用时空皮质刺激来指导动物运动表现的工作，我们已经做好了进行这些实验并取得成功的准备。实验将在恒河猴身上进行，最初使用虚拟环境手臂，然后使用机器人外骨骼包裹猴子自己的手臂，训练它们抓住物体。这些猴子将在皮质运动区和体感觉区长期植入多电极阵列。BMI解码算法将从运动皮层植入物记录的神经活动中提取运动命令。这些指令将用于制定由人工执行器执行的伸手和抓握。通过体感皮层植入物传递的ICMS的时空模式将模拟触觉、压力和纹理的感觉。

DESCRIPTION (provided by applicant): This application addresses broad Challenge Area (06) Enabling Technologies and specific Challenge Topic, 06-HD-101 Improved interfaces for prostheses to improve rehabilitation outcomes. Considerable progress has been made during the last decade in the development of BMIs -- devices that translate brain activity into the commands to artificial actuators, such as prosthetic limbs. Neuroprosthetics based on BMI technology aim to restore limb mobility in people suffering from brain injury, neurological diseases and limb loss. One significant unknown in BMI research is the issue of providing sensory feedback from neuroprosthetics to their users. This gap in the field's knowledge impedes the development of clinically relevant neuroprosthetics because normal motor behaviors, which neuroprosthetic systems strive to reproduce, critically depend on the sensory feedback from the skin, muscle and tendon receptors. To address this problem, we propose developing a sensorized neuroprosthesis in which the signals from touch and pressure sensors of a prosthetic hand are delivered to the brain as spatiotemporal patterns of ICMS of the primary somatosensory cortex. We are well positioned to conduct these experiments and achieve success because of our extensive experience with monkey models of BMIs and previous work in which we used spatiotemporal cortical stimulation to guide animal motor performance. The experiments will be conducted in rhesus macaques trained to grip objects initially using a virtual-environment arm and then a robotic exoskeleton encasing the monkey's own arm. These monkeys will be chronically implanted with multielectrode arrays in cortical motor and somatosensory areas. BMI decoding algorithms will extract motor commands from the neural activity recorded by motor cortex implants. These commands will be used to enact reaching and gripping performed by artificial actuators. Sensations of touch, pressure and texture will be mimicked by spatiotemporal patterns of ICMS delivered through the somatosensory cortex implants. PUBLIC HEALTH RELEVANCE: This study is expected to have a significant impact on the development of clinical neuroprosthetics because it will produce effective techniques for sensorizing prosthetic limbs, making them feel as parts of the body and improving the accuracy of manual operations.