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.

0930908Rao脑机接口（BMI）是允许主体使用大脑信号直接控制物体的设备。此类设备允许通过虚拟键盘进行通信并控制假肢机器人设备，从而有可能显着改善被困、瘫痪或残疾人的生活质量。当今脑机接口的两种主要范例依赖于头皮（EEG）的非侵入性记录和基于皮质内植入物的侵入性技术。脑电图信号噪声极大，从而限制了可以可靠提取的控制信号的带宽。另一方面，皮质内植入物会产生更强的信号，但会带来严重的健康风险。在该提案中，PI 描述了一项基于皮质电图 (ECoG) 的 BMI 调查研究项目，这是一种相对较新的技术，涉及从大脑表面硬膜下记录信号。这些信号的信噪比比脑电图信号高得多，同时比穿透大脑表面的技术带来的风险更小。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图像过程中存在高频的广谱ECoG变化。 PI 和他的团队将探索这种 ECoG 调制在 BMI 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团队将研究ECoG光谱变化的动态范围，并分析BMI控制期间由于大脑可塑性而发生的适应。 这将有助于为通过单个控制电极控制 BMI 中的 3 个或更多自由度铺平道路。 (3) 控制信号的抽象：在长时间使用 BMI 后，许多患者报告不再想象移动控制肢体，而是专注于 BMI 任务本身的预期结果。 PI 和他的团队将探索在这种抽象基础上创建新的皮质通信路径，并利用这些新的控制信号来扩展 BMI 的带宽。 (4) 新控制信号在新型 BMI 范式中的应用：BMI 技术将使用虚拟设备（例如用于通信的光标驱动菜单系统）以及更复杂的机器人系统（例如假肢机械手和人形机器人）进行测试。 该项目的教育部分包括课程开发、研究生和本科生的跨学科培训以及对 K-12 学生的推广。 智力优点：拟议的研究代表了利用 ECoG 和大脑可塑性来构建 BMI 的首批努力之一，该 BMI 可以具有大自由度的控制装置。控制信号抽象及其在机器人 BMI 中的应用的研究也是新颖的。 更广泛的影响：如果成功，这项研究将带来新的基于 ECoG 的 BMI 系统，该系统将依靠大脑的适应能力超越当前 BMI 的能力新颖的控制场景并利用 ECoG 测量的大规模人群电活动。该项目将使研究生能够在多学科环境中接受培训。有前途的本科生，包括来自代表性不足群体的学生，将获得宝贵的研究经验，为工业和学术生涯做好准备。 K-12 的外展工作将使当地学校的学生能够参观 PI 的实验室，并获得新兴脑机接口领域的实践经验。