Non-Invasive System for Identifying Neural Behavior

用于识别神经行为的非侵入性系统

• 批准号: 7489322
• 负责人: Carlo J De Luca
• 金额: $ 45.51万
• 依托单位: ALTEC, INC.
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-01 至 2010-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7489322
• 关键词: Action Potentials; Age; Aging; Algorithms; Amyotrophic Lateral Sclerosis; Artificial Intelligence; Arts; Basic Science; Behavior; Behavioral; Businesses; Cerebral Palsy; Characteristics; Clinical; Code; Communities; Computer software; Contracts; Data Collection; Detection; Development; Disease; Electrodes; Electromyography; Ensure; Exercise; Exercise Physiology; Exposure to; Fatigue; Feedback; Fire - disasters; Functional disorder; Goals; Grant; Individual; International; Invasive; Left; Marketing; Measurement; Measures; Methodology; Microgravity; Modeling; Motor; Motor Neurons; Muscle; Muscle Contraction; Muscle Fibers; NIH Program Announcements; Needles; Neurons; Numbers; Parkinson Disease; Pattern; Performance; Principal Component Analysis; Principal Investigator; Procedures; Process; Production; Publishing; Purpose; Range; Rate; Reliance; Research; Research Personnel; Right-On; Risk; Safety; Sensitivity and Specificity; Series; Shapes; Side; Signal Transduction; Skin; Source; Space Medicine; Specificity; Standards of Weights and Measures; Stroke; Surface; System; Techniques; Technology; Testing; Text; Time; Tongue; Training; Validation; Work; assault; base; commercialization; data acquisition; design; ergonomics; experience; human subject; improved; innovative technologies; instrumentation; interest; mathematical model; motor control; neuromuscular system; neuroregulation; neurotechnology; new technology; non-invasive system; performance tests; processing speed; programs; prototype; relating to nervous system; research and development; research study; response; sensor; technology development; technology/technique

DESCRIPTION (provided by applicant): This application for "Neurotechnology Research, Development, and Enhancement" (PA-04-006) proposes the development of innovative technologies, methodologies, and instrumentation to advance our understanding of neural control mechanisms of muscle force production through a non-invasive means of recording neuronal firing patterns. The project will develop an automatic system to accurately and quickly decompose the surface Electromyographic (sEMG) signal into its constituent action potentials and provide the timing of the firings of concurrently active motor units. The goal is to achieve an accuracy of >85% in the automatic mode and >96% with the assistance of an interactive editor. This information will enable a wide range of studies to investigate the workings of the healthy and diseased neuromuscular system by simply placing a sensor above a muscle with no assault to the CNS. The sEMG Decomposition System will replace existing technology that relies on invasive procedures to detect the EMG signal through needle or fine-wire electrodes. The proposed work includes: 1) mathematical modeling and empirical studies to develop a sEMG electrode array that maximizes shape differences of motor unit firings and thereby facilitates sEMG signal decomposition; 2) algorithm development using artificial intelligence technology of our own design combined with Principal Component Analysis techniques; and 3) data acquisition/processing software and hardware to build a portable prototype surface decomposition system. Performance testing of the system will be conducted using data collection experiments to ensure that the system is comparable in motor unit yield, processing speed, and accuracy to the current state-of-the art indwelling decomposition system. We will also prove that the signal decomposition is performed correctly by decomposing two separately collected signals and matching the results. A dissemination plan is included to make this technology available to the Motor Control community. Commercialization will be realized through Altec Inc. This technology will enable researchers in the fields of Motor Control, Aging, Exercise Physiology, Space Medicine, and Ergonomics, where it is of interest to understand how the CNS controls muscles, and how that control is altered as a consequence of aging, exercise, exposure to microgravity, fatigue, and excessive and prolonged force production. It will be useful to clinicians for assessing the degree of dysfunction in upper motoneuron diseases such as Cerebral Palsy, Parkinson's Disease, ALS, Stroke, and other disorders.

描述（由申请人提供）：本“神经技术研究、开发和增强”申请 (PA-04-006) 提出开发创新技术、方法和仪器，以通过以下方式增进我们对肌肉力量产生的神经控制机制的理解：记录神经元放电模式的非侵入性方法。该项目将开发一种自动系统，能够准确、快速地将表面肌电图 (sEMG) 信号分解为其组成动作电位，并提供同时活动的运动单元的放电时间。目标是在自动模式下实现 >85% 的准确度，在交互式编辑器的帮助下实现 >96% 的准确度。这些信息将使广泛的研究能够通过简单地将传感器放置在肌肉上方而不会攻击中枢神经系统来调查健康和患病神经肌肉系统的运作情况。 sEMG 分解系统将取代依赖侵入性程序通过针或细线电极检测 EMG 信号的现有技术。拟议的工作包括：1）数学建模和实证研究，以开发 sEMG 电极阵列，最大限度地提高运动单元放电的形状差异，从而促进 sEMG 信号分解； 2）采用自主设计的人工智能技术结合主成分分析技术进行算法开发； 3）数据采集/处理软件和硬件，以构建便携式原型表面分解系统。该系统的性能测试将通过数据收集实验进行，以确保该系统在运动单位产量、处理速度和准确性方面与当前最先进的留置分解系统相当。我们还将通过分解两个单独收集的信号并匹配结果来证明信号分解是正确执行的。其中包括一项传播计划，以使该技术可供电机控制社区使用。商业化将通过 Altec Inc. 实现。这项技术将使运动控制、衰老、运动生理学、太空医学和人体工程学领域的研究人员能够了解中枢神经系统如何控制肌肉以及如何改变这种控制由于衰老、锻炼、暴露于微重力、疲劳以及过度和长时间的用力产生。它将有助于临床医生评估上运动神经元疾病（如脑瘫、帕金森病、ALS、中风和其他疾病）的功能障碍程度。