Brain Machine Interface for Controlling Neuroprosthesis for Reaching and Grasping

用于控制神经假体进行触及和抓取的脑机接口

• 批准号: RGPIN-2016-06358
• 负责人: Popovic, Milos
• 金额: $ 3.35万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=710261
• 关键词: Brain; Machine; Interface; Controlling; Neuroprosthesis

Neuroprostheses are devices that use bursts of low energy electrical pulses to generate muscle contractions. If the pulses are properly sequenced they can generate movements such as reaching, grasping and walking in individuals who are paralyzed. Since 2001, our team invested considerable amounts of intellectual capital to develop neuroprostheses for reaching and grasping. What differentiates our technology from other similar systems is that our neuroprostheses are not used as permanent orthoses patients need to use at all times to reach and grasp. Instead, we use neuroprosthesis as a short-term therapeutic intervention that helps stroke and spinal cord injury patients “reprogram” their central nervous system, and relearn how to reach and grasp objects on their own. We call this new way of using neuroprstheses the Functional Electrical Stimulation Therapy (FEST). Following FEST and after the neuroprosthesis is removed, the patients are able to reach and grasp on their own without help from the device. Brain Machine Interfaces (BMIs) are systems that use electrical signals recorded from the brain to control external devices such as computers and robotic arms. Given that they do not require any movement (i.e. muscle contraction) to enable the system control, the BMI systems hold enormous potential to assist profoundly disabled individuals such as those with severe stroke and spinal cord injury, to perform tasks they are unable to carry out independently. In 2015 our team discovered that if the FEST is controlled by a BMI system, which is triggered by brain signals associated with hand opening and closing, that such therapy generates stronger and more beneficial neuroplastic changes in the brain as compared to conventional FEST training alone. Therefore, the objective of this research program is to develop a variety of BMI technologies suitable for integration with FEST and to create first clinically viable BMI+FEST intervention. In order to control FEST with a BMI system, the BMI has to be able to determine: (i) what reaching and grasping movement(s) the patient intends to perform; (ii) the speed with which the patient would like these tasks to be performed; and (iii) initial and end positions of the arm during task execution. Ideally, this should be a non-invasive, electroencephalogram (EEG) -based BMI system. The specific objectives of this research program are: Task-1: Develop a BMI system able to detect the hand posture (e.g. pinch and lateral grasps) using EEG recordings. Task-2: Develop a BMI system able to detect the velocity and position of the hand in 3D space using EEG recordings. Task-3: Develop a self-contained hardware and software platform that incorporates BMI systems from Tasks 1 and 2. Task-4: Explore use of microelectrode recording and operant conditioning of single neurons as a means to generate control signals to be used to control FEST.

神经假体是使用低能量电脉冲的突发来产生肌肉收缩的设备。如果脉冲顺序正确，它们可以在瘫痪的人身上产生伸手、抓握和行走等动作。自2001年以来，我们的团队投入了大量的智力资本来开发用于伸手和抓握的神经假体。我们的技术与其他类似系统的区别在于，我们的神经假体不是用作患者需要始终使用以达到和抓取的永久矫形器。相反，我们使用神经假体作为短期治疗干预，帮助中风和脊髓损伤患者“重新编程”他们的中枢神经系统，并重新学习如何自己到达和抓住物体。我们称这种使用神经假体的新方法为功能性电刺激疗法（FEST）。FEST后和神经假体取出后，患者能够在没有器械帮助的情况下自己够到并抓握。 脑机接口（Brain Machine Interfaces，简称BMI）是一种使用大脑记录的电信号来控制计算机和机械臂等外部设备的系统。由于BMI系统不需要任何运动（即肌肉收缩）来实现系统控制，因此它具有巨大的潜力，可以帮助严重残疾的人（如患有严重中风和脊髓损伤的人）执行他们无法独立执行的任务。2015年，我们的团队发现，如果FEST由BMI系统控制，该系统由与手开合相关的大脑信号触发，与传统的FEST训练相比，这种治疗会在大脑中产生更强，更有益的神经可塑性变化。 因此，本研究计划的目标是开发适合与FEST整合的各种BMI技术，并创建第一个临床可行的BMI+FEST干预。为了用BMI系统控制FEST，BMI必须能够确定：（i）患者打算执行什么触及和抓握运动;（ii）患者希望执行这些任务的速度;以及（iii）任务执行期间手臂的初始和结束位置。理想情况下，这应该是一个非侵入性的，基于脑电图（EEG）的BMI系统。这项研究计划的具体目标是：任务-1：开发一个BMI系统，能够使用EEG记录检测手的姿势（例如捏和横向抓）。任务2：开发一个BMI系统，能够使用EEG记录检测3D空间中手的速度和位置。任务3：开发一个独立的硬件和软件平台，将任务1和任务2中的BMI系统整合在一起。任务四：探索使用微电极记录和单个神经元的操作性条件反射作为产生用于控制FEST的控制信号的手段。