共查询到16条相似文献,搜索用时 59 毫秒
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基于大脑皮层信息传输的脑电信息图示方法 总被引:4,自引:0,他引:4
提出一种基于大脑皮层信息传输的脑电地形图示方法—脑电信息图(Brain InformationMapping - BIM) 。其原理是从不同导联电极上采集脑电信号经相空间重建构成头皮电位信息传输矩阵, 将各导联信息传输时间序列的信息传输量和复杂度数据绘制成头皮拓扑分布图, 以直观地反映脑电信息传输分布模式在不同时相中的变化进程。该方法不仅是从新的角度观察大脑功能变化, 而且可克服传统的脑电频谱分段地形图不能表达长程脑电模式变化的不足。对局限性癫痫病患者的试用表明,脑电信息图能较好地反映癫痫发作前后的信息传输动向和复杂度(Kc 、C1 、C2) 的变化趋势。结果提示,脑电信息图(BIM) 有可能成为一种新的观察大脑功能活动的图示诊断方法,值得进一步深入研究。 相似文献
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建立了一个急性高空缺氧实验模型,记录了四种不同高度条件下从缺氧前(正常呼吸)到缺氧后30分钟时的EEG,分析了其复杂度。发现缺氧引起复杂度明显变化,随时间和高度增加,一定程度缺氧可使EEG复杂度低于正常。表明EEG复杂度对脑缺氧较为敏感,可用于缺氧程度进行评估,有望成为临床诊断的一个指标。 相似文献
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局灶性脑缺血的早期无创诊断在临床实际中有着非常重要的意义。采用SD(Sparague-Dawley)大鼠建立了局灶性脑缺血的动物实验模型,记录了缺血前后缺血区域和正常区域的脑电信号EEG。由于近似熵复杂度算法所需时间序列长度较短,大大减少了脑电信号非平稳所带来的困难,且无需粗粒化,采用近似熵对局灶性缺血动物实验模型的脑电信号的复杂度进行了分析。结果发现缺血前后缺血与非缺血区域的近似熵均有着易于区分的特征,因此EEG信号的近似熵分析可以用于对局灶性缺血的脑损伤程度进行诊断,并区分损伤区域和非损伤区域,有望在临床中加以应用。 相似文献
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不同状态下脑电图复杂性探索 总被引:12,自引:2,他引:12
Lempel-Ziv所定义的有限序列的复杂性反映了给定序列随其长度的增长出现新模式的速率,事实上它反映了序列接近随机的程度。将该复杂性度量运用于脑电分析,旨在克服分数维方法的缺陷。文中计算了八种实验条件下脑电图的复杂度,涉及看、听、休息和心算等基本的大脑功能状态,13个被试的16导数据被用于计算分析.结果显示了复杂度在不同电极位置及实验条件下都有变化,睁眼状态的复杂度高于闭眼,而施加任务时有额部大脑活动区域复杂度降低的现象。同时复杂度也提供了一些研究大脑高级认知活动的新思路。 相似文献
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局部肌肉疲劳的表面肌电信号复杂度和熵变化 总被引:6,自引:0,他引:6
目的 在于探讨静态和动态疲劳性运动过程中肱二头肌和腰部脊竖肌表面肌电(surface electromyography,sEMG)信号的Lempel-Ziv复杂度和Kolmogorov熵的变化规律。18名男性大学生志愿者被随机分为肱二头肌和腰部脊竖肌运动负荷组,分别完成静态和动态疲劳运动负荷试验。运动负荷期间连续记录sEMG信号,在对运动负荷时间和重复次数进行标准化处理后,截取相应时段的sEMG信号,计算Lempel-Ziv复杂度和Kolmogorov熵,观察它们随肌肉疲劳发展的变化规律。研究结果表明,无论是静态还是动态疲劳运动条件下,被检肌肉sEMG信号的复杂度和熵均随着运动负荷时间呈现明显的单调递减型变化。该变化可能与神经系统渐进性协调众多运动单位同步收缩的‘协同效应”有关。 相似文献
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Massimiliano Ignaccolo Mirek Latka Wojciech Jernajczyk Paolo Grigolini Bruce J. West 《Journal of biological physics》2010,36(2):185-196
The scaling properties of human EEG have so far been analyzed predominantly in the framework of detrended fluctuation analysis (DFA). In particular, these studies suggested the existence of power-law correlations in EEG. In DFA, EEG time series are tacitly assumed to be made up of fluctuations, whose scaling behavior reflects neurophysiologically important information and polynomial trends. Even though these trends are physiologically irrelevant, they must be eliminated (detrended) to reliably estimate such measures as Hurst exponent or fractal dimension. Here, we employ the diffusion entropy method to study the scaling behavior of EEG. Unlike DFA, this method does not rely on the assumption of trends superposed on EEG fluctuations. We find that the growth of diffusion entropy of EEG increments of awake subjects with closed eyes is arrested only after approximately 0.5 s. We demonstrate that the salient features of diffusion entropy dynamics of EEG, such as the existence of short-term scaling, asymptotic saturation, and alpha wave modulation, may be faithfully reproduced using a dissipative, first-order, stochastic differential equation—an extension of the Langevin equation. The structure of such a model is utterly different from the “noise+trend” paradigm of DFA. Consequently, we argue that the existence of scaling properties for EEG dynamics is an open question that necessitates further studies. 相似文献
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In order to be able to simulate long time and large space scale properties of polymer melts one has to resort to coarse grained models, for example by subdividing all polymers into parts and restricting attention to the center of mass positions and velocities of these parts. The dynamics of these variables is governed by Langevin equations in which the free energy obtained by integrating the remaining variables provides the potential of the conservative forces. In general this leads to many particle interactions on the coarse-grained level. Methods suggested in the literature to represent these many particle interactions by effective two body interactions are reviewed and a new method, based on the Gibbs-Bogoliubov inequality, is proposed. The reason why none of these methods is able to reproduce the pressure of the underlying atomistic model is discussed. 相似文献
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Guosheng Yi Jiang Wang Hongrui Bian Chunxiao Han Bin Deng Xile Wei Huiyan Li 《Cognitive neurodynamics》2013,7(1):79-88
To explore the effects of manual acupuncture (MA) on brain activities, we design an experiment that acupuncture at acupoint ST36 of right leg with four different frequencies to obtain electroencephalograph (EEG) signals. Many studies have demonstrated that the complexity of EEG can reflect the states of brain function, so we propose to adopt order recurrence quantification analysis combined with discrete wavelet transform, to analyze the dynamical characteristics of different EEG rhythms under acupuncture, further to explore the effects of MA on the complexity of brain activities from multi-scale point of view. By analyzing the complexity of five EEG rhythms, it is found that the complexity of delta rhythm during acupuncture is lower than before acupuncture, and for alpha rhythm that is higher, but for beta, theta and gamma rhythms there are no obvious changes. All of those effects are especially obvious during acupuncture with frequency of 200 times/min. Furthermore, the determinism extracted from delta, alpha and gamma rhythms can be regarded as a characteristic parameter to distinguish the state acupuncture at 200 times/min and the state before acupuncture. These results can provide a theoretical support for selecting appropriate acupuncture frequency for patients in clinical, and the proposed methods have the potential of exploring the effects of acupuncture on brain activities. 相似文献