ECoG Correlates of Visuomotor Transformation and Application to a Force-Based BCI

描述(由申请人提供)：本提案的长期目标是开发一种新的脑机接口(BCI)算法，旨在应用于瘫痪肢体的功能性神经肌肉刺激。为了实现这一总体目标，这项建议的第一个目标是确定可能有助于我们的脑机接口追求的运动皮质区域的特性。具体地说，我们的第一个目标将寻求识别当任务(即用于完成任务的计算机光标)的末端效应器的运动与导致该任务的物理运动分离时，在运动相关皮质区域的运动参数的脑电地形图(ECoG)表示中的差异和灵活性。以前的研究表明，运动皮质上的高伽马频段ECoG和LFP活动(&gt；60赫兹)与完成任务时的手速有很好的相关性，而单单元研究表明，运动前皮质区域可能比物理运动本身更涉及到达运动的计划和/或感知。然而，这些研究通常采用将手臂的物理动作线性转换为末端执行器的视觉反馈来完成任务。为了进一步研究跨皮质区域运动表征的差异，我们将训练三只雄性恒河猴(Macaca Mulatta)进行2-D中心向外操纵杆任务，在该任务中，操纵杆的位置(用作手位置的替代物)重新映射到面向目标的计算机光标的位置、速度或加速度，从而消除操纵杆和光标之间关于速度和加速度重新映射方案的任何线性关联。我们将首先检查植入在M1、PMD和顶区5上的阵列的硬膜外ECoG频谱与操纵杆或光标的运动参数的相关性，并比较不同重新映射方案的这些相关性，以确定特定区域与运动的规划/感知方面还是与其物理执行更相关。我们的第二个目标是利用这些ECoG表征的特性，并确定猴子是否可以学习在脑机接口(BCI)任务中调制基于力的控制信号。到目前为止，大多数脑机接口实验都利用了基于运动学的控制信号(即大脑信号被转换为物体的位置或速度)。然而，这些控制信号可能不适合于在个体手臂的准确模型未知或不完整的情况下对瘫痪肢体进行功能性神经肌肉刺激(FNS)等应用。为了解决这个问题并评估基于动力学的控制算法的可行性，该算法可能更适合于FNS，我们将在基于力的BCI任务中训练猴子。在这些实验中，高伽马(75-105赫兹)ECoG活动将被映射到计算机光标上的虚拟作用力，在类似中心外的任务中，计算机光标将被用来“抓取”和移动有质量的外围目标。虚拟环境还将结合简单的现实世界物理，如重力和可变质量，以评估受试者在现实生活中使用感兴趣的控制算法适应这些扰动的能力。

DESCRIPTION (provided by applicant): The long-term objective of this proposal is to develop a novel brain computer interface (BCI) algorithm targeted for use in applications such as functional neuromuscular stimulation of paralyzed limbs. Toward this overall goal, the first aim of this proposal seeks to identify properties of motor cortical areas that may aid in our BCI pursuits. Specifically, our first objective will seek to identify differences and flexibility in electrocorticography (ECoG) representations of movement parameters across motor-associated cortical areas when movement of the end-effector for a task (i.e. a computer cursor used to complete the task) is dissociated from the physical movement leading up to it. Previous studies have shown that high gamma band ECoG and LFP activity (>60 Hz) over motor cortex is well correlated to hand speed during reaching tasks, while single unit studies have suggested that premotor cortical areas may be more involved in the planning and/or perception of reaching movements than the physical movement itself. However, these studies usually employed a linear transformation of the physical arm movements to visual feedback of the end-effector for the task. To further investigate the differences in representation of movement across cortical areas, we will train three male rhesus monkeys (Macaca mulatta) on a 2-D center-out joystick task in which position of the joystick (used as a proxy for hand position) is remapped to the position, velocity, or acceleration of a goal-oriented computer cursor, thus removing any linear correlations between joystick and cursor for velocity and acceleration remapping schemes. We will first examine the epidural ECoG spectra of arrays implanted over M1, PMd, and parietal area 5 for correlations with movement parameters of the joystick or cursor, and compare these correlations across remapping schemes to identify whether a particular area is more correlated with planning/perceptual aspects of a movement or with its physical execution. Our second objective is to utilize properties of these ECoG representations and determine whether monkeys can learn to modulate a force-based control signal in a brain- computer interface (BCI) task. The majority of BCI experiments to date have utilized kinematic-based control signals (i.e. a brain signal is translated to the position or velocity of an object). However, these control signals may not be suitable for applications such as functional neuromuscular stimulation (FNS) of a paralyzed limb where an accurate model of an individual's arm is unknown or incomplete. To address this problem and assess the feasibility of a dynamics-based control algorithm that may be more appropriate for FNS, we will train monkeys on a force-based BCI task. In these experiments, high gamma (75-105 Hz) ECoG activity will be mapped to a virtual force on a computer cursor in a center-out like task in which the computer cursor will be used to "grab" and move peripheral targets with mass. The virtual environment will also incorporate simple real-world physics such as gravity and variable masses to assess how well a subject might be able to adapt to these perturbations in real-life with the control algorithm of interest.