Development of a Bidirectional Brain Machine Interface

描述（由申请人提供）：在短短几年内，脑机接口（BMI）已经从科幻小说变成了科学好奇心，成为一门快速发展的工程学科，具有临床重要性的真正潜力。从大脑中记录的信号可能被用来控制无生命的物体，这一认识吸引了大众和科学界的兴趣。然而，尽管在关注和科学工作方面有了巨大的增长，两个基本的限制仍然存在：1)绝大多数bmi只从大脑中提取运动学（位置）信息，忽略了同样存在于初级运动皮层中的与力相关的丰富信息；2)几乎所有现有的bmi都完全依赖自然视觉来指导运动，缺乏对正常运动至关重要的快速本体感觉反馈。我们建议在上一个赠款周期取得的进展的基础上解决这两个限制。我们之前证明了关节扭矩和肌电图预测的准确性与运动学预测相当。我们现在建议使用这些信息作为基于转矩的控制器和自适应混合转矩位置控制器的基础。解码器将使用来自初级运动皮层和背侧运动前皮层的输入。我们假设这种方法将允许猴子受试者执行更现实的任务，这些任务需要在不断变化的动态环境中运动。两个典型的例子是需要抓住和移动一个物体，以及需要控制端点力和位置，例如，在书写时。我们还证明，视觉引导的BMI表现可以通过增加自然本体感觉而得到改善，并且猴子可以在皮层的本体感觉区域区分不同强度的电刺激。我们现在建议刺激这些区域，为猴子提供人工本体感觉反馈。我们将用特定的模式来刺激特定的电极，以模拟猴子在运动过程中肢体受到干扰时产生的信号。我们假设，刺激将导致猴子在一个由刺激的特定特征决定的方向上启动一个短潜伏期纠正。最终，我们提出将混合自适应控制器与本体感觉假肢结合起来，并测试猴子对这两种界面的适应能力。我们假设，大脑皮层的可塑性变化，加上算法适应，将在几天到一周的时间内推动性能的提高。拟议中的实验将直接导致对大脑运动和感觉区域编码信号的更清晰理解，以及当患者从中风、截肢或脊髓损伤等神经和肌肉骨骼疾病中恢复时至关重要的适应过程。此外，我们预计，随着这项技术从实验阶段进入临床阶段，它将直接使这些患者受益。

DESCRIPTION (provided by applicant): In a scant few years, the Brain Machine Interface (BMI) has gone from science fiction to a scientific curiosity, to a rapidly growing engineering discipline with real potential for clinical importance. The realization that signals recorded from the brain might be used to control inanimate objects has captured the fascination of the popular and scientific communities alike. However, despite tremendous increase in attention and scientific work, two fundamental limitations remain: 1) The great majority of BMIs extract only kinematic (position) information from the brain, ignoring the wealth of force-related information that is also present in the primary motor cortex, and 2) virtually all existing BMIs depend exclusively on natural vision to guide movement, lacking the rapid proprioceptive feedback that is critical for normal movement. We propose to address both of these limitations by building on the progress we have made in the previous grant cycle. We previously demonstrated both joint torque and EMG predictions with accuracy comparable to that of kinematic predictions. We now propose to use this information as the basis both for a torque-based controller, and an adaptive, hybrid torque-position controller. The decoder will use inputs from both primary motor cortex and the dorsal premotor cortex. We hypothesize that this approach will allow the monkey subjects to perform more realistic tasks that require movement in a changing and changing dynamical environment. Two typical examples are the need to grasp and move an object, and the need to control both endpoint force and position, for example, when writing. We have also demonstrated that visually guided BMI performance can be improved with the addition of natural proprioception, and that monkeys can discriminate electrical stimuli of different intensity in proprioceptive areas of the cortex. We now propose to stimulate these areas to provide artificial proprioceptive feedback to the monkey. We will stimulate particular electrodes with patterns intended to mimic the signals that occur when the monkey's limb is perturbed during the movement. We hypothesize that the stimulation will cause the monkey to initiate a short latency correction in a direction determined by the particular characteristics of the stimulation. Ultimately we propose to combine the hybrid, adaptive controller with the proprioceptive prosthesis, and to test the monkey's ability to adapt to the two interfaces. We postulate that that plastic changes in the cortex, combined with algorithmic adaptation will drive improvements in performance with a time course of several days to a week. The proposed experiments will lead directly to clearer understandings of the signals encoded in both the motor and sensory areas of the brain, and the adaptive processes that are critical when a patient recovers from neurological and musculoskeletal disorders like stroke, amputation, or spinal cord injury. Furthermore, we anticipate that the developed technology will directly benefit these same patients as it is moved from the experimental arena to the clinic. PUBLIC HEALTH RELEVANCE: Brain machine interface (BMI) technology now allows both human and animal subjects to control the position of a robotic limb, but it does not yet work well to apply controlled forces to objects, and it cannot provide normal proprioceptive feedback to the subject. Our proposed work mimics several aspects of the intact nervous system by providing direct control of force through a torque-decoding BMI that adapts in response to position errors. In addition to natural vision, the monkey subjects will receive artificial proprioceptive feedback about the movement through electrical microstimulation of the brain.