CAREER: Characterizing and Optimizing Control in Neural Interfaces

职业：表征和优化神经接口控制

• 批准号: 2338662
• 负责人: Amy Orsborn
• 金额: $ 89.93万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-07-01 至 2029-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2338662&HistoricalAwards=false
• 关键词: CAREER; Characterizing; Optimizing; Control; Neural

This Faculty Early Career Development Program (CAREER) award supports research that aims to expand our understanding of human-technology interaction through Brain-Computer Interfaces (BCI). This award will support foundational research into how the nervous system responds to the use of neural interfaces, paving the way for the creation of advanced computer algorithms that can adapt to the user's nervous system. As BCI create intricate connections between the nervous system and technology by measuring biological signals from individuals and translating them into commands for devices, and they hold great potential for treating neurological disorders. Neural interface technologies have the potential to revolutionize the field of rehabilitation by allowing the nervous system to control novel devices that can offer new hope and possibilities for regaining control and independence despite physical limitations. However, developing computer algorithms that can effectively interact with the human nervous system remains a challenge. The interdisciplinary nature of this research will draw on the PI’s expertise in neuroscience, control theory, and neural engineering. Additionally, this award will support the creation of new outreach programs and integrate the findings into engineering courses while encouraging participation from underrepresented groups in engineering.Closed-loop interactions between a user and the device in a neural interface open opportunities to leverage nervous system plasticity to improve performance and shape user behavior for rehabilitation. Achieving this goal requires scientific insights into how nervous systems interact with devices and new computational frameworks to jointly consider the device, the nervous system, and their interactions. This project will identify principles of how users learn to control sensorimotor neural interfaces and use these insights to improve computational methods for closed-loop neural interfaces. The PI's team will perform experiments using two types of neural interfaces—muscle interfaces in humans and brain interfaces in non-human primates to understand computations performed by the nervous system when learning to control an interface and whether properties of the device influence these computations. The PI will quantify neural computations using a control theoretic framework that can measure users' predictive models of the device. Insights from these experiments will be used to improve user models, which will, in turn, be used to design new interface algorithms that will be experimentally validated against additional muscle interface experiments.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这项教师早期职业发展计划（职业）奖支持旨在通过脑部计算机界面（BCI）扩展我们对人类技术互动的理解的研究。该奖项将支持有关神经系统如何对神经接口的使用响应的基础研究，从而为创建可以适应用户神经系统的高级计算机算法铺平了道路。由于BCI通过测量个体的生物学信号并将其转化为设备的命令，在神经系统与技术之间建立了复杂的联系，并且它们具有治疗神经系统疾病的巨大潜力。神经界面技术有可能通过允许神经系统控制新型设备来彻底改变康复领域，从而为重新控制和独立物理限制提供新的希望和可能性。但是，开发可以有效与人类神经系统相互作用的计算机算法仍然是一个挑战。这项研究的跨学科性质将借鉴PI在神经科学，控制理论和神经工程方面的专业知识。此外，该奖项将支持创建新的外展计划，并将调查结果整合到工程课程中，同时鼓励代表性不足的团体参与工程。用户与设备之间的循环 - 环境相互作用在神经元素界面之间的开放机会开放机会，以利用神经系统的可变性，以提高恢复性的性能和塑造恢复性的性能。实现这一目标需要科学见解，即神经系统如何与设备和新的计算框架相互作用，以共同考虑设备，神经系统及其相互作用。该项目将确定用户如何学习控制感觉运动神经接口的原则，并使用这些见解来改善闭环神经接口的计算方法。 PI的团队将使用两种类型的神经界面进行实验：人类中的人类界面和非人类隐私的脑界面，以了解神经系统在学习控制接口时执行的计算以及设备的属性是否影响这些计算。 PI将使用控制理论框架来量化神经元计算，该框架可以测量用户的设备预测模型。这些实验的洞察力将用于改善用户模型，这反过来将用于设计新的界面算法，这些算法将对其他肌肉界面实验进行实验验证。该奖项反映了NSF的法定任务，并通过使用该基金会的知识分子和更广泛的影响来评估诚实地将其视为诚实的支持。