NRI:BMI Control of a Therapeutic Exoskeleton

描述（由申请人提供）：本研究旨在通过利用患者意图和损伤实时评估的好处，加速中风后机器人康复的发展、疗效和使用。验证将在德克萨斯州休斯顿的康复研究所（TIRR）使用MAHI EXO-II外骨骼机器人进行。机器人康复是脑卒中患者感觉运动训练的有效平台。机器人设备能够准确定位受损肢体，同时提供辅助和阻力，并收集运动数据，可用于表征患者的运动质量。MAHI EXO-II是一种物理人机界面，将增强非侵入性脑机接口（BMI），将患者纳入控制回路，从而使治疗“活跃”，并使患者参与康复任务中各种损伤严重程度的治疗。该方法利用了在其他机器人康复临床研究中观察到的患者在运动开始时意图的已知益处，以及使用BMI对皮质可塑性的有益影响。机器人测量

DESCRIPTION (provided by applicant): This research aims to accelerate the development, efficacy and use of robotic rehabilitation after stroke by capitalizing on the benefits of patient intent and real-time assessment of impairment. Validation will occur using the MAHI EXO-II exoskeleton robot at The Institute for Rehabilitation and Research (TIRR) in Houston, Texas. Robotic rehabilitation is an effective platform for sensorimotor training in stroke patients. A robotic device enables accurate positioning of the impaired limb while simultaneously providing assistance & resistance forces and collection of motion data that can be used to characterize the quality of the patient's movements. The MAHI EXO-II, a physical human-robot interface, will be augmented with a non-invasive brain-machine interface (BMI) to include the patient in the control loop, thereby making the therapy 'active' and engaging patients across a broad spectrum of impairment severity in the rehabilitation tasks. This approach capitalizes on the known benefits of patient intent in movement initiation observed in other clinical studies of robotic rehabilitation and on the beneficial effects of BMI use on cortical plasticity. Robotic measures of motor impairment, derived from real-time data acquired from sensors on the MAHI EXO-II and from the BMI, will drive patient-specific therapy sessions adapted to the capabilities ofthe individual, with the robot providing assistance or challenging the participant as appropriate, in order to maximize rehabilitation outcomes. Assist-as-needed paradigms in robotic rehabilitation have been shown to be efficacious; however, such paradigms are passive and driven by performance metrics that have not been sufficiently validated and verified. Additionally, intense practice and continual 'challenge' during therapy is known to improve rehabilitation outcomes. Key contributions: 1) Adapting most advanced EEG- BMI methods to stroke patients and developing a BMI for the control of the MAHI EXO-II that will a) increase upper limb function, b) advance understanding of brain plasticity, and c) innovate rehabilitation; 2) Determining appropriate robotic and electrophysiological measures of motor impairment and associated control algorithms for patient-specific therapy; and 3) Clinical validation in pilot studies to evaluate the proposed approach.