Reach and grasp by people with tetraplegia using a neurally controlled robotic arm

摘要

John Donoghue及其同事发现，四肢瘫痪的人能学习使用来自运动皮层的神经信号来控制电脑光标。另一个实验室所做的工作也表明，猴子也能学习使用这样的信号来用一个机械臂给自己喂食。现在，Donoghue及其同事将这一技术提升到了一个新水平：两个长期瘫痪的人(一个是58岁的女性，另一个是66岁的男性)能够用一个神经界面指挥一个机械臂去拿东西和抓东西。其中一个人能够学会用5年前植入的一个设备来捡起一个瓶子并从中饮水，这不仅说明实验对象能够使用大脑一机器界面，而且说明该界面有潜在的长寿命。%Paralysis following spinal cord injury, brainstem stroke, amyotrophic lateral sclerosis and other disorders can disconnect the brain from the body, eliminating the ability to perform volitional movements. A neural interface system could restore mobility and independence for people with paralysis by translating neuronal activity directly into control signals for assistive devices. We have previously shown that people with long-standing tetraplegia can use a neural interface system to move and click a computer cursor and to control physical devices. Able-bodied monkeys have used a neural interface system to control a robotic arm, but it is unknown whether people with profound upper extremity paralysis or limb loss could use cortical neuronal ensemble signals to direct useful arm actions. Here we demonstrate the ability of two people with long-standing tetraplegia to use neural interface system-based control of a robotic arm to perform three-dimensional reach and grasp movements. Participants controlled the arm and hand over a broad space without explicit training, using signals decoded from a small, local population of motor cortex (MI) neurons recorded from a 96-channel micro-electrode array. One of the study participants, implanted with the sensor 5 years earlier, also used a robotic arm to drink coffee from a bottle. Although robotic reach and grasp actions were not as fast or accurate as those of an able-bodied person, our results demonstrate the feasibility for people with tetraplegia, years after injury to the central nervous system, to recreate useful multidimensional control of complex devices directly from a small sample of neural signals.