Decoding of Force from Neural Signals in Motor Cortex

运动皮层神经信号中力的解码

基本信息

批准号：
8839285
负责人：
JAMES ASHE
金额：
--
依托单位：
MINNEAPOLIS VA MEDICAL CENTER
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-08-01 至 2016-07-31
项目状态：
已结题

项目摘要

DESCRIPTION (provided by applicant): The advent of brain-machine interface (BMI) technology has brought with it the expectation that it will be a realistic and practical treatment option for patients with limb amputation or spinal crd injury. However, significant limitations in current approaches that result in a relatively low leve of functionality of prosthetic control need to be addressed before this expectation can be realized. In the current application, we outline three specific aims that tackle some of the major obstacles standing in the way of BMI utility, and propose related hypotheses that address fundamental issues in motor neuroscience. Aim 1: To examine the efficacy and reliability of decoding force output from neural signals in primary motor cortex and dorsal pre-motor cortex during reaching movements in a force field. Our hypothesis is that the neural signals related to the force (kinetic) trajectory of the arm can be decoded in real-time from neural signals in motor and premotor cortex. Aim 2: To test the generalization of force decoding performance across different force fields and over time. Our hypothesis, is that the spatial-temporal patterns of neural activity in multi- channel recordings are specific for the behavioral state (direction of foce trajectory in this case) and that such patterns are robust over time and across different motor environments. Aim 3: To characterize the neural signal associated with self-initiated motor output (movement and force) in motor and pre-motor cortex. Our hypothesis is that the neural signal reflecting self- initiated voluntary motor behavior is detectable in motor areas and can be used to implement 'free-paced' BMI control. The specific aims we propose will be addressed using neural and behavioral data from experiments in non-human primates trained to make reaching movements to spatial targets in the presence of distinct force fields that mimic stiffness, viscosity, and inertia; properties of the motor environment that we deal with on a daily basis. The neural data will be recorded from chronically implanted multi-electrode arrarys and mico-electroencephalography (ECoG) arrays in primary motor (M1) and dorsal premotor (PMd) cortex, We plan to study several different neural signals including single unit activity (SUA), multi-unit activity (MUA), local field potentials (LFP) and ECoG which will give us the added advantage of being able to compare the quality of decoding across the different neural signals

描述（由申请人提供）：脑机界面（BMI）技术的出现带来了人们的期望，即对肢体截肢或脊柱CRD损伤的患者成为现实且实用的治疗选择。但是，在实现这一期望之前，需要解决导致假体控制功能相对较低的当前方法的显着局限性。在当前的应用中，我们概述了三个特定的目标，可以解决一些主要的障碍，以BMI实用性的方式解决，并提出相关假设，以解决运动神经科学中基本问题的问题。 AIM 1：检查在力场中触及运动过程中，原代运动皮层和背运动前皮层中神经信号的解码力输出的功效和可靠性。我们的假设是，可以从运动和前皮层中的神经信号实时解码与手臂的力（动力学）轨迹相关的神经信号。目标2：测试在不同力场和随着时间的推移之间的力量解码性能的概括。我们的假设是，多通道记录中神经活动的时空模式特异性是行为状态（在这种情况下，FOCE轨迹的方向），并且随着时间的推移和跨不同运动环境，此类模式是强大的。 AIM 3：表征与运动和运动前皮层中的自发电动机输出（运动和力）相关的神经信号。我们的假设是，可以在运动区域检测到反映自愿运动行为的神经信号，可用于实施“自由节奏”的BMI控制。我们提出的具体目的将使用来自经过训练的非人类灵长类动物的实验中的神经和行为数据来解决，以在存在明显的力场的情况下，以模仿刚度，粘度和惯性的不同力场，以实现运动到空间目标。我们每天处理的运动环境的属性基础。神经数据将记录在慢性植入的多电极arrarys和电脑电图（ECOG）阵列（M1）和背侧（PMD）皮质中的阵列（ECOG）阵列，我们计划研究几种不同的神经信号，包括单个单位活动（SUA），多单位活性（MUA），局部电位（MUA），以及comp of to thit to and to to to to and to and to and commans（MUA），以及cog commant of to to comm and comm commans to yous to compts to yous comp of to compts to yous comm commans（lfp）（lfp）（lfp）（lfp）跨不同神经信号解码的质量