Action Potentials vs. Field Potentials as Inputs to a Brain-Machine Interface

动作电位与场电位作为脑机接口的输入

基本信息

批准号：
8091226
负责人：
Marc W. Slutzky
金额：
$ 16.78万
依托单位：
NORTHWESTERN UNIVERSITY AT CHICAGO
依托单位国家：
美国
项目类别：
财政年份：
2007
资助国家：
美国
起止时间：
2007-08-01 至 2013-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8091226
关键词：
Action Potentials Address Algorithms American Amyotrophic Lateral Sclerosis Benchmarking Biological Neural Networks Brain Cerebral Palsy Cervical spinal cord injury Clinical Research Complex Computer Simulation Craniotomy Data Development Plans Discriminant Analysis Disease Dura Mater Electrodes Electroencephalography Environment Evaluation Figs - dietary Fostering Frequencies Genetic Programming Goals Hand Implant Implanted Electrodes Knowledge Learning Macaca Macaca mulatta Machine Learning Measures Mentors Methods Modeling Monkeys Motor Motor Cortex Movement Muscle Neurons Operative Surgical Procedures Output Patients Performance Physicians Pontine structure Principal Investigator Procedures Process Quadriplegia Rattus Relative (related person)Research Research Personnel Resolution Scalp structure Scientist Signal Transduction Source Spinal cord injury Statistical Methods Stroke Sum Supervision System Techniques Time Tissues Training Visual arm brain machine interface career development computerized data processing cranium design disability feeding flexibility improved in vivo independent component analysis motor control novel programs prototype relating to nervous system research study surgical technique development

项目摘要

DESCRIPTION (provided by applicant): Over 600,000 Americans have severely impaired motor function from disorders including spinal cord injury, amyotrophic lateral sclerosis, pontine stroke, and cerebral palsy. A brain-machine interface (BMI) could enable locked-in or tetraplegic patients to communicate and interact with their environment. Two crucial decisions in designing a BMI are (1) what type of brain signals to use as inputs to a controller and (2) what methods to use to decode those signals. Most BMIs have used either noninvasive scalp EEG recordings or invasive intracortical recordings of single- or multi-neuron spikes as control inputs. A few have used subdural or intracortical local field potentials (LFPs). However, no group has yet systematically compared these signals in motor cortex for use in BMI applications. This proposal's first goal is to assess the relative performance of spikes and field potentials (both intracortical and epidural) as control inputs for a variety of movement-related outputs. Epidural field potentials (EFPs) are intermediate in invasiveness, signal quality, stability and spatial resolution compared with existing scalp, subdural, and intracortical recordings, and thus represent an unexplored middle ground. This proposal's second goal is to evaluate linear and nonlinear techniques-including several novel to BMI applications-for both decoding data and reducing the inherently large dimensionality of data from multiple neural signals. The primary hypotheses of the proposed project are (1) that spikes will perform better in decoding more complex movement-related outputs, but that field potentials may perform similarly on decoding simpler outputs, and (2) that nonlinear decoders and dimensionality-reduction techniques may provide improved accuracy over linear methods. The specific aims to address these hypotheses are 1) to evaluate single neuron spikes as inputs to decoders of movement- related outputs, 2) to develop a novel epidural multi-electrode recording technique in the macaque monkey, and 3) to evaluate field potential signals as inputs to decoders of movement-related outputs. Aims 1 and 3 will involve application of dimensionality-reduction algorithms (e.g., independent components analysis, Isomap) and decoding algorithms (system identification, neural networks, support vector machines) to both spikes and field potentials. Aim 2 will entail using a computer model and spatial spectral analysis to optimize the epidural electrode array design. This project will provide the first comparison of spikes, LFPs and EFPs as inputs for identical BMI output applications. The supervision of Drs. Lee Miller and W. Zev Rymer, with additional guidance from Drs. Simon Levine, Jonathan Wolpaw and Nicholas Hatsopoulos, will provide the principal investigator with expertise in recording and processing both spikes and field potentials for BMI applications using a variety of state-of-the- art techniques. A comprehensive career development plan including clinical and research mentoring, seminars, and courses, will foster the candidate's transition into an independent physician-scientist.

描述（由申请人提供）：超过60万名美国人因疾病，包括脊髓损伤，肌萎缩性侧索硬化，蓬蒂因中风和脑瘫的疾病严重损害了运动功能。脑机界面（BMI）可以使锁定或四边形患者能够与环境进行交流和互动。设计BMI的两个关键决定是（1）用于控制器的输入的哪种类型的大脑信号以及（2）用于解码这些信号的方法。大多数BMI都使用非侵入性头皮脑电图记录或单神经位或多神经位峰的侵入性内部记录作为对照输入。一些使用了硬膜下或心脏内局部田间电位（LFP）。但是，尚无系统地比较运动皮层中的这些信号以用于BMI应用。该提案的第一个目标是评估尖峰和田间电位（磁性内和硬膜外）的相对性能，作为各种运动相关输出的控制输入。与现有的头皮，硬膜下和心脏记录相比，硬膜外场电位（EFP）的侵入性，信号质量，稳定性和空间分辨率是中间的，因此代表了未探索的中间立场。该提案的第二个目标是评估线性和非线性技术，包括对数据进行解码数据，并降低来自多个神经信号的数据的固有较大维度。拟议项目的主要假设是（1）尖峰在解码更复杂的运动相关输出方面的性能会更好，但是该场电位可能会在解码更简单的输出上类似地表现，并且（2）非线性解码器和维度还原性降低技术可能会提高线性方法的精确度。解决这些假设的具体目的是1）评估单个神经元的尖峰作为对运动相关输出的分解器的输入，2）开发一种新型的硬膜外硬膜外多电极记录技术，以及3）以评估现场电位信号作为对流动输出的解码器的输入。 AIM 1和3将涉及将维数减少算法（例如独立的组件分析，ISOMAP）和解码算法（系统识别，神经网络，支持向量机）应用于峰值和现场电位。 AIM 2将需要使用计算机模型和空间光谱分析来优化硬膜外电极阵列设计。该项目将提供尖峰，LFP和EFP的首次比较，作为相同BMI输出应用程序的输入。博士的监督。李·米勒（Lee Miller）和W. Zev Rymer，以及Drs的其他指导。西蒙·莱文（Simon Levine），乔纳森·沃尔波（Jonathan Wolpaw）和尼古拉斯·哈索普洛斯（Nicholas Hatsopoulos）将为首席研究人员提供使用各种最先进技术的BMI应用程序记录和处理尖峰和现场潜力的专业知识。一项全面的职业发展计划，包括临床和研究指导，研讨会和课程，将促进候选人向独立的医师科学家的过渡。