CAREER: Generalizable, Robust, and Closed-Loop Brain-Machine Interface Control Architectures

职业：通用、鲁棒、闭环脑机接口控制架构

• 批准号: 1453868
• 负责人: Maryam Shanechi
• 金额: $ 50.31万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-02-15 至 2024-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1453868&HistoricalAwards=false
• 关键词: CAREER; Generalizable; Robust; Closed; Loop

Brain-machine interfaces (BMI) aim to restore movement in millions of disabled people. Despite successful laboratory demonstrations, the lack of generalizability and robustness, and the low performance remain key challenges hindering clinical viability. BMIs should be able to control a variety of prosthetic devices, and to exploit any neural signal modality as their control signal. This research develops generalizable, robust, and closed-loop BMI architectures for neuroprosthetic control, and applies these architectures both to build proficient neuroprosthetics and to investigate the brain mechanisms underlying such control. This research is fully integrated with outreach and education activities including interactions with disabled Veterans, and mentoring of women and underrepresented minorities.One main reason for the lack of generalizability and robustness in existing BMIs is that they do not model the behavior of the single common component in all BMI settings, i.e., the brain, which controls the movement. Moreover, performance of current BMIs has been sacrificed because they have not been adapted to the statistical properties of the recorded neural signal modality and have used standard signal processing algorithms for any modality. This research develops a BMI that can control prosthetics with various dynamics and using different neural recording modalities. It builds a novel model of the brain in closed-loop control, constructs principled stochastic models for different recording modalities, and combines these two models to devise an adaptive supervised learning and decoding algorithm. It also uses the architecture to investigate the brain mechanisms underlying neuroprosthetic control. This research enables a universal and principled neuroprosthetic architecture, replacing ad-hoc approaches; it significantly improves neuroprosthetic performance; finally, it allows for a deeper understanding of the fundamental brain mechanisms underlying neurprosthetic and motor control.

脑机接口（BMI）旨在恢复数百万残疾人的运动能力。尽管成功的实验室演示，缺乏通用性和稳健性，低性能仍然是阻碍临床可行性的主要挑战。bmi应该能够控制各种假肢装置，并利用任何神经信号模式作为其控制信号。本研究为神经义肢控制开发了可推广的、鲁棒的、闭环的BMI结构，并将这些结构应用于构建熟练的神经义肢和研究这种控制背后的大脑机制。这项研究与外联和教育活动充分结合，包括与残疾退伍军人的互动，以及对妇女和代表性不足的少数民族的指导。现有BMI缺乏通用性和稳健性的一个主要原因是，它们没有模拟所有BMI设置中单个共同组成部分的行为，即控制运动的大脑。此外，由于目前的bmi没有适应记录的神经信号模态的统计特性，并且对任何模态都使用标准的信号处理算法，因此牺牲了其性能。本研究开发了一种BMI，可以用不同的动态和不同的神经记录方式来控制假肢。建立了一种新颖的闭环控制大脑模型，构建了不同记录方式的原则性随机模型，并将这两种模型结合起来设计了一种自适应监督学习和解码算法。它还使用该架构来研究神经假肢控制背后的大脑机制。这项研究使一个通用的和原则性的神经假肢架构，取代特设的方法；它显著提高了神经假肢的性能；最后，它允许更深入地了解神经假肢和运动控制的基本大脑机制。