Electrocorticographic Brain-Machine Interfaces for Communication and Prosthetic Control

用于通信和假肢控制的皮质电脑机接口

• 批准号: 0930908
• 负责人: Rajesh Rao
• 金额: $ 30万
• 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: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0930908&HistoricalAwards=false
• 关键词: Electrocorticographic; Brain; Machine; Interfaces; Communication

0930908RaoBrain-machine interfaces (BMIs) are devices that allow a subject to control objects directly using brain signals. Such devices offer the potential to significantly improve the quality of life of locked-in, paralyzed, or disabled individuals by allowing them to communicate via virtual keyboards and control prosthetic robotic devices. The two dominant paradigms for brain-machine interfacing today rely on non-invasive recording from the scalp (EEG) and invasive techniques based on intracortical implants. EEG signals are extremely noisy, thereby limiting the bandwidth of control signals that can be reliably extracted. Intracortical implants on the other hand yield stronger signals but pose serious health risks. In this proposal, the PI describes a research program for investigating BMIs based on electrocorticography (ECoG), a relatively new technique that involves recording signals subdurally from the brain surface. These signals have much higher signal-to-noise ratio than EEG signal while at the same time, pose lesser risks than techniques that penetrate the brain surface. The proposed research will address the following key issues: (1) Exploiting high frequency ECoG signals for BMI: Recent work has shown the existence of broad-spectral ECoG changes at high frequencies during movement and imagery. The PI and his team will explore the application of such ECoG modulation for multi-dimensional control in BMIs. (2) Neural plasticity of local cortical circuits during BMI: The PI's team will investigate the dynamic range of the spectral changes in ECoG and analyze the adaptations that occur due to brain plasticity during BMI control. This will help pave the way for controlling 3 or more degrees of freedom in a BMI from a single control electrode. (3) Abstraction of control signals: After extended periods of BMI use, many patients report no longer imagining moving a control limb but rather concentrating on the desired result of the BMI task itself. The PI and his team will explore the creation of new cortical communication pathways underlying such abstraction and leverage these new control signals in expanding the bandwidth of the BMI. (4) Applications of new control signals to novel BMI paradigms: The BMI techniques will be tested using virtual devices such as cursor-driven menu systems for communication as well as more complex robotic systems such as a prosthetic robotic hand and a humanoid robot. The educational component of the project involves curriculum development, interdisciplinary training for graduate and undergraduate students, and outreach to K-12 students.Intellectual Merit: The proposed research represents one of the first efforts to exploit ECoG and the brain's plasticity to build BMIs that can control devices with large degrees of freedom. The study of abstraction of control signals and its application to robotic BMIs is also novel.Broader Impact: If successful, this research will lead to new ECoG-based BMI systems that will surpass the abilities of current BMIs by relying on the brain's ability to adapt to novel control scenarios and leveraging the large-scale population-level electrical activity measured by ECoG. The project will enable the training of graduate students in a multidisciplinary environment. Promising undergraduates, including students from underrepresented groups, will gain valuable research experience in preparation for industrial and academic careers. A K-12 outreach effort will enable students from local area schools to visit the laboratories of the PIs and gain hands-on experience in the emerging field of brain-machine interfaces.

0930908Raobrain-Machine接口（BMI）是允许受试者使用脑信号直接控制对象的设备。这种设备提供了显着改善锁定，瘫痪或残疾人的生活质量，通过允许他们通过虚拟键盘进行交流并控制假肢机器人设备，以提高锁定，瘫痪或残疾人的生活质量。如今，用于脑部机器接口的两个主要范例依赖于基于心脏内植入物的头皮（EEG）和侵入性技术的非侵入性记录。 EEG信号非常嘈杂，从而限制了可以可靠提取的控制信号的带宽。另一方面，皮质内植入物产生更强的信号，但构成了严重的健康风险。在该提案中，PI描述了一项研究计划，用于研究基于皮质学（ECOG）的BMI，这是一种相对较新的技术，涉及从脑表面细分记录信号。这些信号的信噪比比脑电图信号高得多，而同时，与穿透脑表面的技术相比，风险较小。拟议的研究将解决以下关键问题：（1）利用BMI的高频ECOG信号：最近的工作表明在运动和成像期间高频的广谱ECOG变化存在。 PI和他的团队将探索这种ECOG调制在BMI中的多维控制中的应用。 （2）BMI期间局部皮质回路的神经可塑性：PI的团队将研究ECOG光谱变化的动态范围，并分析由于BMI对照过程中脑可塑性而发生的适应性。 这将有助于为从单个控制电极中的BMI中控制3个或以上的自由度铺平道路。 （3）对照信号的抽象：延长BMI使用时，许多患者报告不再想象移动控制肢体，而是专注于BMI任务本身所需的结果。 PI和他的团队将探讨这种抽象的基础新的皮质通信途径的创建，并利用这些新的控制信号扩大BMI的带宽。 （4）新的控制信号在新颖的BMI范式中的应用：BMI技术将使用虚拟设备（例如Cursor-drive菜单系统进行通信）以及更复杂的机器人系统（例如假体机器人手和人形机器人机器人）进行测试。 该项目的教育部分涉及课程开发，研究生和本科生的跨学科培训，以及向K-12学生推广。智能优点：拟议的研究是利用ECOG的首要研究之一，以利用ECOG和大脑可塑性，以建立可以控制自由度的BMI。对控制信号的抽象研究及其在机器人BMI中的应用也很新颖。Broader的影响：如果成功的话，该研究将导致新的基于ECOG的BMI系统，该系统将通过依靠大脑的能力来适应新的控制场景并利用通过ECOG测量的大型人口级别的电级电动活动，从而超过当前BMI的能力。该项目将在多学科环境中对研究生进行培训。有前途的本科生，包括来自代表性不足的团体的学生，将获得有价值的研究经验，以为工业和学术职业做准备。 K-12外展工作将使当地学校的学生能够参观PIS的实验室，并在脑机界面的新兴领域获得动手经验。