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，如机械肢体。虽然这项技术有很大的潜力使瘫痪的人受益，但bmi通常表现不佳，因为它们使用嘈杂的低水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个缺点，从而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展，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，在BMI技术方面取得重大进展，以促进国民健康，增加对大脑的科学认识，并显著改善这些严重残疾人的生活质量。这个合作项目将从一个四肢瘫痪的人的顶叶皮层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体执行这些意图。将多电极阵列（MEA）植入四肢瘫痪患者的后顶叶皮层（PPC）到达和抓握区，参与实验，探讨认知意图在人类PPC中的神经表征，包括物体选择、动作意图和机械肢体的神经控制。实验结果将用于构建优化的BMI控制算法来解码这些认知信号。与此同时，一种模块化的、半自主的机器人假肢将被开发出来，它可以识别家庭物品，并计划伸手抓握的动作来操纵或运输物品。这些科学和技术努力将得到对四肢瘫痪参与者的持续临床护理的支持。该研究将探索BMI系统越来越强大的迭代，最终在参与者自己的家庭环境中测试完全开发的BMI系统，他们将在那里练习日常生活活动。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，从而大大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.