Electrocorticography signals for human hand prosthetics

人手假肢的皮层电图信号

• 批准号: 8065923
• 负责人: Jeffrey G Ojemann
• 金额: $ 32.24万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-05-01 至 2015-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8065923
• 关键词: Algorithms; Alpha Rhythm; Animals; Area; Behavior; Behavioral; Beta Rhythm; Brain; Brain Injuries; Classification; Collaborations; Coupling; Degenerative Disorder; Development; Devices; Digit structure; Dimensions; Disease; Distant; Electrocorticogram; Electrodes; Epilepsy; Feedback; Fingers; Frequencies; Future; Hand; Hand functions; Human; Image; Imagery; Individual; Lead; Left; Life; Limb structure; Machine Learning; Measures; Motor; Motor Cortex; Movement; Nervous System Physiology; Nervous System Trauma; Nervous system structure; Neurodegenerative Disorders; Neurologic; Neurons; Operative Surgical Procedures; Output; Patients; Pattern; Penetration; Phase; Physics; Population Dynamics; Positioning Attribute; Prosthesis; Resolution; Robotics; Signal Transduction; Source; Spinal cord injury; Stroke; Surface; Survivors; System; Tactile; Technology; Thumb structure; Training; Traumatic Brain Injury; Upper Extremity; Visual; Work; arm; base; brain computer interface; brain machine interface; computer science; disability; functional restoration; grasp; improved; indexing; limb movement; neurosurgery; public health relevance; tool; visual control; visual feedback

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.

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）是导致永久性残疾的主要原因。神经修复领域的最新进展为恢复神经功能的脑机接口的发展提供了巨大的潜力。这个项目将产生一个系统，可以通过大脑表面的记录来控制机械手。直接基于大脑信号的接口可以直接解码控制信号，从而最大限度地提高假肢的效率。这个项目是神经外科、计算机科学和物理系之间的合作，将使用皮质电图（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（bb0 - 75Hz）成分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单个手指运动相关的高频信号成分的理解。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，我们将研究捏握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检查这些运动的行为。在第三个目标中，我们将测量与想象运动相关的ECoG变化，以及当应用于机械手上时，这些变化如何随着视觉反馈而改变。在最终目标中，我们将在控制中加入触觉反馈，以优化基于ecog的手部假肢控制。通过不断提高控制信号的复杂性，以及机器人手输出的复杂性，我们将确定ECoG是否是手部神经假肢装置的可行控制信号来源。