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

点击翻译按钮获取中文摘要

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.