Development of an EMG-controlled BCI for biomimetic control of hand movement in humans

开发 EMG 控制的 BCI，用于仿生控制人类手部运动

• 批准号: 10651404
• 负责人: Jennifer L. Collinger
• 金额: $ 67.8万
• 依托单位: NORTHWESTERN UNIVERSITY AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-18 至 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10651404
• 关键词: 3-Dimensional; Address; Biomimetics; Data; Development; Digit structure; Fingers; Hand; Human; Individual; Intuition; Joints; Kinetics; Leg; Life; Limb structure; Measures; Methods; Modeling; Motion; Motor; Motor Cortex; Motor output; Movement; Muscle; Musculoskeletal; Nerve; Output; Paralysed; Participant; Patients; Pattern; Performance; Persons; Posture; Robotics; Signal Transduction; Spinal cord injury; System; Testing; Time; User-Computer Interface; Visualization; arm; brain computer interface; brain surgery; design; electric impedance; functional electrical stimulation; grasp; high dimensionality; improved; kinematics; limb loss; motor control; neuromuscular system; robot control; virtual reality

Abstract When asked, most persons with high-level spinal cord injury (SCI) would elect brain surgery to improve their hand control, yet even the state-of-the-art intracortical brain computer interfaces (iBCI) have only limited control of finger motion and no direct control of applied forces whatsoever. The exclusive use of kinematics in iBCI control is despite the rich representation of kinetic information in primary motor cortex (M1). We propose to address these fundamental limitations by mimicking the mammalian neuromuscular system, which controls both digit motion and the forces they exert through the modulation of muscle activity. We will develop an iBCI that predicts intended muscle activity (EMG) from M1 recordings in humans, then use these EMG signals to control joint kinematics, their stiffness, and grasp forces, through a forward musculoskeletal model of the hand. We hypothesize that this EMG-based iBCI will be an intuitive means for humans with high-level SCI to achieve more generalizable control of their hand movements than with existing kinematic iBCIs. Decoders for use by paralyzed humans are typically built by recording spiking activity from M1 as the user attempts to imitate the observed motion of a cursor or a robotic arm. The decoder is computed by correlating measured M1 activity with the observed kinematics. Though similar in concept, our approach to decoder development is more challenging, as the high-dimensional motor output signals it requires – the EMGs – cannot be directly visualized or imitated. To circumvent this problem, we will record EMGs (as well as hand posture and contact forces) as able-bodied people perform a broad range of motor actions. We will also record M1 spiking activity as paralyzed individuals observe and attempt to imitate the same actions. The able-bodied EMG data will provide the output signals for decoder calculation, analogous to the use of observed the trajectory in kinematic decoders. This real-time, EMG-based iBCI will allow participants to control a hand, using it to apply forces to grasped objects in a way that mimics natural motor control. Initial development will be done in virtual reality (VR). Subsequently, participants will use the same biomimetic iBCI to control a robotic hand in tasks designed to replicate activities of daily life. We will compare the users' performance with this biomimetic iBCI to that of a state-of-the-art kinematic iBCI. When successful, these methods will have application to the control of robotic limbs for patients with limb loss, and as a means to restore movement of the user's own limbs through Functional Electrical Stimulation. They could also be applied to the legs, where control of interaction forces and limb impedance through muscle cocontraction is also critical.

摘要 当被问及时，大多数患有高位脊髓损伤（SCI）的人会选择脑外科手术来改善他们的生活。 然而，即使是最先进的皮质内脑机接口（iBCI）也只能进行有限的控制 手指的运动和没有直接控制施加的力量。在iBCI中独家使用运动学 尽管在初级运动皮层（M1）中有丰富的运动信息，但控制仍然存在。我们建议 通过模仿哺乳动物的神经肌肉系统来解决这些基本的限制， 手指运动和它们通过肌肉活动的调节施加的力。我们将开发一种iBCI， 从人类的M1记录中预测预期的肌肉活动（EMG），然后使用这些EMG信号来控制 关节运动学，他们的刚度，和把握力，通过一个向前的肌肉骨骼模型的手。我们 假设这种基于EMG的iBCI将是一种直观的手段，用于高级别SCI患者， 与现有的运动学iBCI相比，他们的手部运动的可概括控制。 供瘫痪的人使用的解码器通常通过记录来自M1的尖峰活动来构建， 试图模仿观察到的光标或机器人手臂的运动。解码器通过相关计算 测量的M1活动与观察到的运动学。虽然在概念上相似，但我们的解码器方法 发展更具挑战性，因为它需要的高维运动输出信号-EMG-不能 直接模仿或模仿。为了避免这个问题，我们将记录EMG（以及手的姿势和 接触力），因为身体健全的人执行广泛的运动动作。我们还将记录M1峰值 瘫痪的人观察并试图模仿同样的动作。健全人的肌电图数据 将为解码器计算提供输出信号，类似于使用观察到的轨迹， 运动学解码器这种基于EMG的实时iBCI将允许参与者控制一只手，用它来应用 以一种模仿自然运动控制的方式对抓取的物体施加力。最初的开发将在虚拟 现实（VR）。随后，参与者将使用相同的仿生iBCI来控制机器人手的任务 旨在复制日常生活活动。我们将比较用户的性能与这种仿生iBCI， 最先进的运动iBCI如果成功，这些方法将用于控制 为肢体缺失患者提供的机器人肢体，并作为通过以下方式恢复用户自身肢体运动的手段： 功能性电刺激。它们也可以应用于腿，其中控制相互作用力和 通过肌肉共收缩的肢体阻抗也是关键的。