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: 7675312
• 负责人: Carlo J De Luca
• 金额: $ 44.65万
• 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 2011-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7675312
• 关键词: Action Potentials; Age; Aging; Algorithms; Artificial Intelligence; Arts; Basic Science; Behavior; Behavioral; Businesses; Cerebral Palsy; Characteristics; Clinical; Code; Communities; Computer software; Contracts; Data Collection; Detection; Development; Disease; Electrodes; Ensure; Exercise; Exercise Physiology; Exposure to; Fatigue; Feedback; Functional disorder; Goals; Grant; Individual; International; Left; Marketing; Measurement; Measures; Methodology; Microgravity; Modeling; Motor; Motor Neurons; Muscle; Muscle Contraction; Muscle Fibers; NIH Program Announcements; Needles; Neurons; Parkinson Disease; Pattern; Performance; Principal Component Analysis; Principal Investigator; Procedures; Process; Production; Publishing; Reliance; Research; Research Personnel; Right-On; Risk; Safety; Sensitivity and Specificity; Series; Shapes; Side; Signal Transduction; Skin; Source; Space Medicine; Specificity; 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）：“神经技术研究，开发和增强”的申请（PA-04-006）提出了创新技术，方法论和仪器的发展，以提高我们对通过记录神经元点火模式的肌肉强化神经控制机制的理解。该项目将开发一个自动系统，以准确，快速将表面肌电图（SEMG）信号分解为其组成的动作电位，并提供同时活跃的运动单元发射的时间。目的是在自动模式下达到> 85％的精度，并在交互式编辑器的帮助下达到> 96％。这些信息将使广泛的研究能够通过简单地将传感器放在肌肉上方而没有对CNS攻击的情况下，研究健康和患病的神经肌肉系统的工作。 SEMG分解系统将取代现有的技术，这些技术依赖于侵入性程序以通过针或细丝电极检测EMG信号。拟议的工作包括：1）数学建模和经验研究，以开发一个SEMG电极阵列，该阵列最大化运动单位发射的形状差异，从而促进SEMG信号分解； 2）使用我们自己的设计的人工智能技术与主要组件分析技术相结合的算法开发； 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。系统的性能测试将使用数据收集实验进行，以确保系统单位产量，处理速度和准确性与当前最新的留置分解系统相当。我们还将证明信号分解是通过分解两个单独收集的信号并匹配结果来正确执行的。包括传播计划，以使这项技术可用于运动控制社区。该技术将通过Altec Inc.实现商业化。该技术将使运动控制，衰老，运动生理，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该控制如何因衰老，衰老，运动，锻炼，疲劳，疲劳和过多的作用和延长的力量而改变。对于临床医生评估上运动神经元疾病（例如脑瘫，帕金森氏病，ALS，中风和其他疾病）的功能障碍程度，这将是有用的。

项目成果

Surface EMG signal decomposition using empirically sustainable biosignal separation principles.

使用经验上可持续的生物信号分离原理进行表面肌电信号分解。

• DOI: 10.1109/iembs.2009.5334090
• 发表时间: 2009
• 期刊: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
• 作者: Nawab,SHamid;Chang,Shey-Sheen;DeLuca,CarloJ
• 通讯作者: DeLuca,CarloJ

A model of motoneuron behavior and muscle-force generation for sustained isometric contractions.

持续等长收缩的运动神经元行为和肌肉力量生成模型。

• DOI: 10.1109/iembs.2011.6091012
• 发表时间: 2011
• 期刊: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
• 作者: Contessa,P;Nawab,SH;DeLuca,CJ
• 通讯作者: DeLuca,CJ