NCS-FO: Integrating neural interfaces and machine intelligence for advanced neural prosthetics

脑机接口(BMI)直接从大脑读取信号来控制外部设备，如机械臂。虽然这项技术有很大的潜力让瘫痪的人受益，但BMI的性能往往很差，因为它们使用嘈杂的低电平信号来同时控制机器人肢体运动的许多方面。相比之下，这个项目将通过读取大脑的高级意图来解决这一缺陷，以便控制智能机器人系统。这些变化反映了神经科学和机器智能的前沿进步，需要科学家、工程师和医生之间的密切合作。该项目旨在利用这些不同领域的专业知识来显著改进BMI技术，以促进国民健康，增加对大脑的科学理解，并大幅提高这些严重残疾人的生活质量。这个合作项目将从四肢瘫痪患者的顶叶皮质记录的神经信号中解码高级认知行为，然后使用智能机器人假肢实现这些意图。在后顶叶皮质(PPC)的伸展和抓取区域植入多电极阵列(MEA)的四肢瘫痪患者将参加实验，以探索人类PPC中认知意图的神经表征，包括物体选择、动作意图和机械臂的神经控制。实验结果将被用来构建优化的BMI控制算法，以解码这些认知信号。同时，将开发一种模块化的半自动机器人假肢，可以识别家用物体，并规划伸手和抓取运动，以操纵或运输这些物体。这些科技努力将得到四肢瘫痪参与者持续临床护理的支持。这项研究将探索BMI系统越来越有能力的迭代，最终在参与者自己的家庭环境中测试完全开发的BMI系统，在那里他们将练习日常生活活动。由此产生的系统将利用认知神经科学的深刻见解以及机器传感和机器人控制系统的先进能力，大幅提高脑机接口的易用性和能力。

Brain-machine interfaces (BMI) read signals directly from the brain to control external devices such as robotic limbs. While this technology has great potential to benefit people who are paralyzed, BMIs often have poor performance because they use noisy, low-level signals to simultaneously control many aspects of the robotic limb's movements. In contrast, this project will address this shortcoming by reading high-level intents from the brain in order to control an intelligent robotic system. These changes reflect cutting-edge advances in neuroscience and machine intelligence and will require close cooperation between scientists, engineers, and physicians. The project aims to leverage expertise across these diverse fields in order to generate significant improvements in BMI technology to advance the national health, increase scientific understanding of the brain, and lead to dramatic improvements in the quality of life for these severely disabled persons.This collaborative project will decode high-level cognitive actions from neural signals recorded in the parietal cortex of a tetraplegic human, then carry out those intents using a smart robotic prosthesis. Persons with tetraplegia who have multielectrode arrays (MEA) implanted in reach and grasp areas of the posterior parietal cortex (PPC), will participate in experiments to explore the neural representation of cognitive intentions in human PPC including object selection, action intention, and neural control of robotic limbs. Experimental results will be used to construct BMI control algorithms optimized to decode these cognitive signals. In parallel, a modular, semi-autonomous robotic prosthesis will be developed that can identify household objects and plan reach-and-grasp movements to manipulate or transport the objects. These scientific and technological efforts will be supported by continued clinical care of the tetraplegic participants. The study will explore increasingly capable iterations of the BMI system, culminating in testing of the fully developed BMI system in the participants' own home environment where they will practice activities of daily living. The resulting system will leverage deep insights in cognitive neuroscience and advanced capabilities in machine sensing and robotic control systems to substantially improve the ease of use and capability of brain-machine interfaces.