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，具有真实的临床重要性。 从大脑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，尽管人们的关注和科学工作大大增加，但仍然存在两个基本限制：1）绝大多数BMI仅提取运动学来自大脑的（位置）信息，忽略了也存在于初级运动皮层中的大量力相关信息，以及2）几乎所有现有的BMI都完全依赖于自然视觉来引导运动，缺乏对正常运动至关重要的快速本体感受反馈。 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。 我们以前证明了关节扭矩和EMG预测的准确性可比的运动学预测。 我们现在建议使用此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在不断变化的动态环境中执行需要移动的更现实的任务。 两个典型的例子是需要抓住和移动物体，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性能可以通过增加自然本体感受来改善，并且猴子可以区分皮质本体感受区域中不同强度的电刺激。 我们现在建议刺激这些区域，为猴子提供人工本体感受反馈。 我们将刺激特定的电极与模式，旨在模仿信号发生时，猴子的肢体在运动过程中受到干扰。 我们假设，刺激将导致猴子启动一个短的潜伏期校正的方向所确定的特定特征的刺激。 最终，我们建议将联合收割机的混合，自适应控制器与本体感受假体，并测试猴子的能力，以适应这两个接口。 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改善。 拟议中的实验将直接导致对大脑运动和感觉区域中编码的信号的更清晰的理解，以及当患者从神经和肌肉骨骼疾病（如中风，截肢或脊髓损伤）中恢复时至关重要的适应过程。 此外，我们预计，开发的技术将直接受益于这些相同的患者，因为它是从实验竞技场转移到临床。

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.