CAREER: Characterizing and Optimizing Control in Neural Interfaces

该学院早期职业发展计划（CAREER）奖支持旨在通过脑机接口（BCI）扩大我们对人机交互的理解的研究。该奖项将支持神经系统如何对神经接口的使用做出反应的基础研究，为创建能够适应用户神经系统的先进计算机算法铺平道路。BCI通过测量来自个体的生物信号并将其转换为设备的命令，在神经系统和技术之间建立了复杂的联系，它们在治疗神经系统疾病方面具有巨大的潜力。神经接口技术有可能通过允许神经系统控制新设备来彻底改变康复领域，这些新设备可以为恢复控制和独立性提供新的希望和可能性，尽管存在身体限制。然而，开发能够与人类神经系统有效交互的计算机算法仍然是一个挑战。 这项研究的跨学科性质将利用PI在神经科学，控制理论和神经工程方面的专业知识。此外，该奖项还将支持创建新的推广计划，并将研究成果融入工程课程，同时鼓励工程领域代表性不足的群体参与其中。用户与设备之间在神经接口中的闭环交互为利用神经系统可塑性提高性能和塑造用户行为以进行康复提供了机会。实现这一目标需要对神经系统如何与设备相互作用的科学见解和新的计算框架，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进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接口进行实验-人类的肌肉接口和非人类灵长类动物的大脑接口，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量化神经计算，该框架可以测量用户对设备的预测模型。从这些实验中获得的见解将被用于改进用户模型，而这些模型又将被用于设计新的界面算法，这些算法将通过额外的肌肉界面实验进行实验验证。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

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.