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Analysis of electrical and magnetic bio-signals associated with motor performance and fatigue.

机译:分析与电机性能和疲劳有关的电磁生物信号。

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This dissertation reports findings centered principally on comprehensive research related to human bio-signals (EEG, MEG, EMG and fMRI) acquired during repetitive maximal voluntary contractions (MVC) that induced severe fatigue. Fatigue is a common experience that reduces productivity and quality of life and increases chances of injury. Although abundant information has been gained in the last several decades regarding muscular and spinal-level mechanisms of muscle fatigue, very little is known about how cortical centers control and respond to fatigue. The main purpose of this study was to examine the fatigue effects on the central nervous system by analyzing the bio-signals collected in the designed experiments. Healthy human subjects were asked to perform a series of repetitive handgrip MVCs with their dominant hand until exhaustion. Handgrip forces, electrical activity (EMG) from primary and non-primary muscles, and EEG, MEG, or fMRI signals from different locations of the brain were recorded simultaneously. The time series data were segmented into several physiologically meaningful epochs (time phases), from rest to preparation to movement execution/sustaining. A series of studies, including motor-related cortical potential (MRCP) analysis, power spectrum analysis, time-frequency (spectrogram) analysis of EEG, EEG source localization and nonlinear analysis (fractal dimension and largest Lyapunov exponent), and fMRI analysis, was applied to the data. We hypothesized that the fatigue effects would act differently on brain signals of different phases. The MRCP results showed that the negative potential (NP) related to motor task preparation only had minimal changes with fatigue. The power of all EEG frequencies did not alter significantly during the preparation phase but decreased significantly during the sustained phase of the contraction. The fractal dimension and the largest Lyapunov exponent decreased significantly during the sustained phase as fatigue progressed. On the other hand, the fMRI results only exhibited insignificant fatigue-related reductions of brain activation volume and no significant change of dipole strength derived from multi-channel EEG data. These results have been interpreted by a hypothetical neurophysiological model, in which two groups of cortical neurons (phasic and tonic) are preferentially activated in each physiological phase of the voluntary motor action.
机译:本论文报告的发现主要集中在与引起严重疲劳的反复最大自愿收缩(MVC)过程中获得的人类生物信号(EEG,MEG,EMG和fMRI)相关的综合研究上。疲劳是一种普遍的经历,会降低生产力和生活质量,增加受伤的机会。尽管在过去的几十年中已经获得了有关肌肉和脊髓水平的肌肉疲劳机制的大量信息,但是对于皮质中枢如何控制疲劳和对疲劳作出反应的了解却很少。这项研究的主要目的是通过分析设计实验中收集的生物信号来检查疲劳对中枢神经系统的影响。健康的人类受试者被要求用支配性的手进行一系列重复的手握MVC,直到筋疲力尽。同时记录了握力,主要和非主要肌肉的电活动(EMG)以及来自大脑不同位置的EEG,MEG或fMRI信号。时间序列数据被分为几个生理上有意义的时期(时间阶段),从休息到准备到运动执行/维持。进行了一系列研究,包括运动相关皮层电势(MRCP)分析,功率谱分析,脑电图的时频(频谱图)分析,脑电图源定位和非线性分析(分形维数和最大Lyapunov指数)以及fMRI分析。应用于数据。我们假设疲劳效应对不同阶段的大脑信号的作用不同。 MRCP结果表明,与运动任务准备相关的负电位(NP)仅随疲劳而变化很小。在准备阶段,所有脑电图频率的功率没有明显改变,但在持续的收缩阶段却明显下降。随着疲劳的进行,分形维数和最大的李雅普诺夫指数在持续阶段显着下降。另一方面,功能磁共振成像结果仅显示出与疲劳无关的与大脑激活量无关的减少,而从多通道脑电图数据得出的偶极子强度没有明显变化。这些结果已由一种假设的神经生理模型解释,其中在自主运动的每个生理阶段中,两组皮层神经元(相性和强直性)被优先激活。

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