Kowalik Z J, Elbert T
Institute of Experimental Audiology, University of Muenster, Germany.
Integr Physiol Behav Sci. 1994 Jul-Sep;29(3):270-82. doi: 10.1007/BF02691331.
Depending on the task being investigated in EEG/MEG experiments, the corresponding signal is more or less ordered. The question still open is how can one detect the changes of this order while the tasks performed by the brain vary continuously. By applying a static measurement of the fractal dimension or Lyapunov exponent, different brain states could be characterized. However, transitions between different states may not be detected, especially if the moments of transitions are not strictly defined. Here we show how the dynamical measure based on the largest local Lyapunov exponent can be applied for the detection of the changes of the chaoticity of the brain processes measured in EEG and MEG experiments. In this article, we demonstrate an algorithm for computation of chaoticity that is especially useful for nonstationary signals. Moreover, we introduce the idea that chaoticity is able to detect, locally in time, critical jumps (phase-transition-like phenomena) in the human brain, as well as the information flow through the cortex.
根据脑电图/脑磁图实验中所研究的任务，相应的信号或多或少是有序的。仍然悬而未决的问题是，当大脑执行的任务持续变化时，如何能够检测到这种秩序的变化。通过应用分形维数或李雅普诺夫指数的静态测量，可以表征不同的脑状态。然而，不同状态之间的转变可能无法检测到，特别是如果转变时刻没有严格定义的话。在这里，我们展示了基于最大局部李雅普诺夫指数的动态测量如何能够应用于检测脑电图和脑磁图实验中所测量的大脑过程的混沌性变化。在本文中，我们演示了一种计算混沌性的算法，该算法对于非平稳信号特别有用。此外，我们提出这样一种观点，即混沌性能够在时间上局部地检测人类大脑中的临界跳跃（类似相变的现象）以及通过皮层的信息流。
