Developing a brain-machine interface for an ankle robot

描述(由申请人提供)： 这一建议建立在使用非侵入性脑电图仪(EEG)捕捉大脑激活模式的最新进展的基础上，这些模式可用于控制计算机和/或设备。随着技术先进的机器人矫形器的共同进化，在物理康复中使用这种脑机(BCI)或脑机接口(BMI)的潜力现在是真正的可能性。为下肢开发这样的BMI设备，可以为恢复退伍军人和其他中风和其他神经损伤后残疾的人以及截肢后的活动功能和生活质量提供新的途径。在巴尔的摩退伍军人事务部马里兰运动和机器人卓越中心，我们开发了一种阻抗控制的踝关节机器人(Anklebot)，以增强中风幸存者的瘫痪踝关节运动控制和步态功能。最近，我们还展示了首次使用非侵入性(EEG)来解码和重建非残疾受试者在跑步机上行走时的下肢关节运动学。我们的结果与灵长类动物进行双足跑步机行走时的直接(侵入性)皮质记录非常相似。Anklebot提供了一个理想的测试平台，将这两项研究结合起来，为下肢开发出一种创新的BMI。在这项建议中，我们将验证一种新的神经解码方法，用于提取由Anklebot记录的踝部运动的运动学参数，同时使用高密度脑电在皮质下中风后慢性偏瘫患者和他们的年龄匹配的对照组中进行。我们还将确定解码参数的重测可靠性。这两组受试者都将在佩戴Anklebot的同时接受训练，以产生大脑激活模式，以自主控制Anklebot，这证明了概念证明，可以通过使用皮质信号来操作下肢动力矫形器。据我们所知，这将是下肢BMI应用的第一次演示。具体地说，这个试点项目旨在测试这样一个假设，即高密度EEG可以可靠地解码与Anklebot进行的背屈(DF，PF)运动相关的不同的大脑激活模式。根据我们以前使用相同方法训练上肢BCI控制的经验，我们预计根据EEG信号重建脚踝运动将产生足够高的相关性和信噪比，以允许从解码的EEG中进行闭环学习，从而对照组和中风组将学习生成特定的EEG模式，以控制Anklebot通过预期的DF-PF运动进行移动。这项研究将提供第一个概念的证据，即使用非侵入性脑电可以通过意图控制用于下肢的“智能”假肢。这些结果将证明我们的长期目标的可行性，即开发一种用于两足运动的思想控制外骨骼。

DESCRIPTION (provided by applicant): This proposal builds on recent advances in using noninvasive electroencephalograhy (EEG) to capture brain activation patterns that can be used to control computers and/or devices. The potential to use such brain-computer (BCI) or brain-machine interfaces (BMI) in physical rehabilitation is now a real possibility with the co-evolution of technologically sophisticated robotic orthoses. Developing such BMI devices for the lower extremites could provide new avenues for restoring mobility function and quality of life among Veterans and others who are disabled after stroke and other neurological injuries, as well as lower limb amputation. In the Baltimore VA's Maryland Exercise and Robotics Center of Excellence, we have developed an impedance controlled ankle robot (anklebot) to enhance paretic ankle motor control and gait function in stroke survivors. Recently we also have shown the first use of noninvasive (EEG) to decode and reconstruct lower extremity joint kinematics for treadmill walking in nondisabled subjects. Our results are very similar to those obtained with direct (invasive) cortical recordings from primates as they performed bipedal treadmill walking. The anklebot provides an ideal test platform to integrate these two lines of research to deveop an innovative BMI for the lower extremity. In this proposal we will validate a novel neural decoding approach for extracting kinematic parameters for ankle movements recorded with the anklebot while using high-density EEG in persons with chronic hemiparesis after subcortical stroke and their age-matched controls. We will also determine the test-retest reliability of the decoded parameters. Both groups of subjects will be trained while wearing the anklebot to generate brain activation patterns to autonomously control the anklebot, demonstrating proof-of-concept that a lower extremity powered orthoses can be operated by using cortical signals. To our knowledge this will be the first demonstration of a lower extremity BMI application. Specifically, this pilot project is designed to test the hypothesis that high density EEG can reliably decode distinct brain activation patterns associated with dorsi-plantarflexion (DF, PF) movements performed in the anklebot. Based on our prior experience using the same approach to train BCI control for the upper extremity, we expect that reconstruction of ankle movements from the EEG signals will yield correlations and signal-to-noise ratios sufficiently high to permit closed loop learning from the decoded EEG, such that the control group and the stroke group will learn to generate specific EEG patterns for controlling the anklebot to move through intended DF-PF movements. This study will provide the first proof of concept that "smart" prostheses for the lower extremity can be controlled by intention using noninvasive EEG. These results will demonstrate feasibility for our long-range aim of developing a thought-controlled exoskeleton for bipedal locomotion.