Electrocorticographic Brain-Machine Interfaces for Communication and Prosthetic Control

[09:30908]脑机接口（bmi）是一种允许受试者使用大脑信号直接控制对象的设备。这种设备可以通过虚拟键盘和控制假肢机器人设备进行交流，从而有可能显著提高闭锁、瘫痪或残疾人士的生活质量。目前脑机接口的两种主要模式依赖于来自头皮的非侵入性记录（EEG）和基于皮质内植入的侵入性技术。脑电图信号具有极大的噪声，从而限制了能够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，但会带来严重的健康风险。在这一提案中，PI描述了一个基于皮质电图（ECoG）研究bmi的研究项目，这是一种相对较新的技术，涉及记录大脑表面硬膜下的信号。这些信号比脑电图信号具有更高的信噪比，同时比穿透大脑表面的技术具有更小的风险。该研究将解决以下关键问题：(1)利用高频ECoG信号研究BMI：最近的研究表明，在运动和成像过程中，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频谱变化的动态范围，并分析BMI控制期间由于大脑可塑性而发生的适应。这将有助于为从单个控制电极控制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的基础上创造新的皮层通信途径，并利用这些新的控制信号来扩大BMI的带宽。(4)新的控制信号在新的BMI范式中的应用：BMI技术将使用虚拟设备（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（如假肢机械手和类人机器人）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。智力优势：这项提议的研究代表了利用ECoG和大脑的可塑性来构建可以控制大自由度设备的bmi的首次努力之一。控制信号的抽象及其在机器人bmi中的应用也是一个新的研究方向。更广泛的影响：如果成功，这项研究将导致新的基于ECoG的BMI系统，该系统将依靠大脑适应新控制情景的能力，并利用ECoG测量的大规模人群电活动，从而超越当前BMI的能力。该项目将能够在多学科环境中培训研究生。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准备。一项K-12的推广工作将使当地学校的学生能够参观pi的实验室，并在新兴的脑机接口领域获得实践经验。

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.