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Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
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对EEG微态序列的复杂度测量:概念和算法

Frederic von Wegner1, Milena Wiemers2, Gesine Hermann3

  • 1School of Biomedical Sciences, University of New South Wales (UNSW), Wallace Wurth, Kensington, NSW, 2052, Australia. f.vonwegner@unsw.edu.au.

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|September 26, 2023
PubMed
概括
此摘要是机器生成的。

这项研究为分析大脑电活动 (EEG) 微态序列引入了新的复杂度指标. 深度睡眠阶段显示大脑活动模式的随机性降低,统计复杂性增加.

科学领域:

  • 神经科学是一个神经科学.
  • 复杂性科学 复杂性科学
  • 信息理论 信息理论
关键词:
复杂性 复杂性的电脑电脑电图微状态电脑电图 (电脑电图) 是一种脑电图.Entropy Entropy赫斯特的指数是一个指数.马尔科夫模型的模型

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背景情况:

  • 脑电图微态序列分析量化了大脑电活动动态.
  • 现有的方法在不同的时间尺度上探索各种复杂性概念.
  • 过量的是微观状态研究中未被充分探索的数量.

结论:

  • 深度睡眠阶段与Kolmogorov复杂度降低和统计复杂度增加相关.
  • LZC提供了高效的率估计,而联合估计则产生了过多的.
  • 统计复杂度指标通过解决尚未探索的复杂性概念来增强微态分析.