A primate model of an intra-cortically controlled FES prosthesis for grasp
A primate model of an intra-cortically controlled FES prosthesis for grasp
批准号:
7750515
负责人:
Lee Miller
金额:
$31.83万
依托单位国家:
美国
项目类别:
财政年份:
2006
资助国家:
美国
项目状态:
已结题
起止时间:
2006-01-01 至 2011-06-30
关键词:
AccountingAlgorithmsAreaAttentionBrainCervicalConsciousContractsDataDevelopmentDiscriminationElbowElectric StimulationElectrodesEnvironmentForearmFreedomGoalsHandHumanImplantIndividualInjuryIntramuscularLateralLearningLocal AnestheticsMapsMeasurementMeasuresMethodsModelingMonkeysMotorMotor CortexMovementMuscleNerveNerve BlockNeuronsParalysedPatientsPatternPerformancePrimatesProcessProsthesisQuadriplegiaResearch PersonnelSamplingSeriesShoulderSignal TransductionSpinal cord injuryStagingStimulusSystemTechniquesTestingTimeUniversitiesUpper Extremityarmbasebrain computer interfacedesigngraspimproved functioningmedian nerveneuromuscularneuroprosthesisprograms
中文摘要
该项目的目标是开发上肢神经肌肉刺激的灵长类动物模型
系统通过皮质内记录电极控制。患有脊髓损伤的人会变得
瘫痪是因为他们失去了激活肌肉的能力。这些病人的肌肉仍然可以
如果通过直接施加到肌肉或神经的电刺激来激活它们,则它们会收缩。
同样,通常控制运动的大脑区域仍然活跃,但它们与大脑的连接
肌肉因受伤而丧失。
凯斯西储大学 (CWRU) 的研究人员证明,植入的功能性
电刺激(FES)神经假体可用于恢复患有以下疾病的个体的抓握功能
四肢瘫痪。尽管引人注目,但这些系统仅限于预编程的抓取模式,并且需要
相当有意识的关注。一个更自然、具有更多自由度的控制系统可以
提供大大改进的功能。在西北大学,我们开发了预测活动的方法
基于大脑微电极记录的抓握动作中的手臂和手部肌肉
猴子。从单个长期植入的电极阵列中,可以预测
肩部、手臂和手部肌肉。这种类型的电极已在
超过3年的时间,最近已被批准用于人类患者的实验。我们相信
像这样的皮质内记录提供了同时控制多个程度的潜力
通过自然思维过程获得自由。结合西北大学和 CWRU 的优势
小组中,我们建议开发一种足以控制神经假体的脑机接口。的
开发这种神经假体系统的灵长类动物模型将是朝着它的方向迈出的重要一步
在人类患者中实施。该应用程序包括以下具体目标:
1)我们建议使用植入猴子初级运动皮层的 100 个电极阵列来提供
输入到一组解码器,旨在对特定手部肌肉的活动进行实时预测。
2)我们建议使用目标1中开发的控制算法和植入的FES假体来恢复
药物神经阻滞引起暂时性肌肉麻痹后的抓握。 3)我们建议
开发这些控制算法而不使用初始肌电图测量,这是必要的
为了为患者实施该系统。
英文摘要
The goal of this project is to develop a primate model of an upper extremity neuromuscularstimulation
system controlled by means of intra-cortical recording electrodes. Individuals with spinal cord injury become
paralyzed because they have lost the ability to activate their muscles. These patients' muscles can still be
made to contract if they are activated by means of electrical stimuli applied directly to the muscle or nerves.
Likewise, the areas of the brain that normally control movement are still active, but their connection to the
muscles has been lost as a result of the injury.
Researchers at Case Western Reserve University (CWRU) have demonstrated that implanted functional
electrical stimulation (FES) neuroprostheses can be used to restore grasp functions to individuals with
tetraplegia. Although remarkable, these systems are limited to pre-programmed grasp patterns, and require
considerable conscious attention. A more natural control system, with more degrees of freedom could
provide greatly improved function. At Northwestern, we have developed methods to predict the activity of
arm and hand muscles during grasping movements based on micro-electrode recordings from the brain of a
monkey. From a single, chronicallyimplanted array of electrodes, predictionscan be made of the activity of
shoulder, arm and hand muscles. This type of electrode has yielded maintained recordings for periods in
excess of 3 years, and it has recently been approved for experimental use in human patients. We believe that
intra-cortical recordings like these provide the potential for simultaneouscontrol of multipledegrees of
freedom through natural thought processes. By combining the strengths of the Northwestern and CWRU
groups, we propose to develop a brain-computer interface adequate for controlling a neuroprosthesis. The
development of a primate model of this neuroprosthetic system would be a major step toward its
implementation in human patients. This application includes the following specific aims:
1) We propose to use a 100-electrode array implanted in the primary motor cortex of a mnkey to provide the
input to a set of decoders designed to produce real-time predictions of the activity of particular hand muscles.
2) We propose to use the control algorithms developed in aim 1 and an implanted FES prosthesis to restore
grasp followingtemporary muscle paralysis induced by a pharmacological nerve block. 3) We propose to
develop these control algorithms without the use of initial EMG measurements, as would be necessary in
order to implement the system for a patient.
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