Electrocorticography signals for human hand prosthetics

描述（由申请人提供）：神经损伤（如中风、创伤性脑损伤和脊髓损伤）是导致永久残疾的主要原因。 神经修复学领域的最新进展为脑机接口的发展提供了巨大的潜力，以恢复神经功能。 这个项目将导致一个系统，可以控制一个机器人的手使用记录从大脑表面。 直接基于大脑信号的接口可以允许控制信号的直接解码，以实现最大效率的假肢。 这个项目是神经外科、计算机科学和物理系之间的合作，将利用皮层电图（ECoG）来探索手部运动背后的大脑信号。 我们之前已经表明，ECoG的高频（> 75 Hz）分量携带有关局部大脑活动的信息。 在第一个目标中，我们将扩展我们对与个人手指运动相关的高频信号分量的理解。 我们将使用PCA从非特异性α和β节律中提取ECoG的宽带变化，并使用机器学习算法增强手指分类。 在第二个目标中，我们将寻找反映不同手部功能的控制信号，而不是不同手指的运动。 例如，我们将研究捏和抓行为是否会产生更多可分离的高频ECoG信号。 我们还将在更高的空间分辨率下研究这些运动的行为。 在第三个目标中，我们将测量与想象运动相关的ECoG变化，以及当应用于机器人手时，这些变化如何随着视觉反馈而改变。 在最后的目标，我们将增加触觉反馈的控制，以优化基于ECoG的手假肢的控制。 通过不断提高控制信号的复杂性和机器人手部输出的复杂性，我们将确定ECoG是否是手部神经假体设备的可行控制信号来源。 公共卫生相关性：开发一种手神经假体，或与神经系统相互作用的人工装置，可以恢复中风、脑损伤、脊髓损伤或神经退行性疾病患者的功能，这些疾病损害了手或手臂的使用。该项目研究是否可以使用直接从人脑记录的信号（在癫痫手术期间）来控制机器人手。

DESCRIPTION (provided by applicant): Neurological injury (such as from stroke, traumatic brain injury, and spinal cord injury) is a major cause of permanent disability. Recent advances in the field of neuroprosthetics hold enormous potential for the development of brain-computer interfaces to restore neurological function. This project will lead to a system that can control a robotic hand using recordings from the surface of the brain. Interfaces based directly from brain signals may allow for direct decoding of control signals for maximally efficient prosthetics. This project, a collaboration between neurosurgery, computer science, and physics departments, will explore the brain signals underlying hand movement using electrocorticography, or ECoG. We have previously shown that high frequency (>75Hz) components of the ECoG carry information about local brain activity. In the first aim, we will expand our understanding of the high-frequency signal components that correlate with individual finger movements. We will extract broadband changes in ECoG from non-specific alpha and beta rhythms using PCA and enhance finger classification with machine learning algorithms. In the second aim, we will look for control signals reflecting different hand functions, rather than movement of different fingers. For instance, we will examine if pinch and grasp behaviors give more separable high- frequency ECoG signals. We will also examine the behavior of these movements at higher spatial resolution. In the third aim, we will measure ECoG changes associated with imagined movement and how these changes are altered with visual feedback when applied to a robotic hand. In the final aim, we will add tactile feedback to the control to optimize ECoG-based control of a hand prosthesis. By increasingly advancing the complexity of the control signal, and the complexity of the robotic hand output, we will establish if ECoG is a viable source of control signal for a hand neuroprosthetic device. PUBLIC HEALTH RELEVANCE: The development of a hand neuroprosthetic, or artificial device that interacts with the nervous system could restore function to those afflicted by stroke, brain injury, spinal cord injury, or neurodegenerative diseases that have damaged the use of a hand or arm. This project examines whether signals recorded directly from the human brain (during surgery for epilepsy) could be used to control a robotic hand.