Cogn Neurodyn. 2011 Jun;5(2):171-82. doi: 10.1007/s11571-011-9151-3. Epub 2011 Feb 9.
To understand the nature of brain dynamics as well as to develop novel methods for the diagnosis of brain pathologies, recently, a number of complexity measures from information theory, chaos theory, and random fractal theory have been applied to analyze the EEG data. These measures are crucial in quantifying the key notions of neurodynamics, including determinism, stochasticity, causation, and correlations. Finding and understanding the relations among these complexity measures is thus an important issue. However, this is a difficult task, since the foundations of information theory, chaos theory, and random fractal theory are very different. To gain significant insights into this issue, we carry out a comprehensive comparison study of major complexity measures for EEG signals. We find that the variations of commonly used complexity measures with time are either similar or reciprocal. While many of these relations are difficult to explain intuitively, all of them can be readily understood by relating these measures to the values of a multiscale complexity measure, the scale-dependent Lyapunov exponent, at specific scales. We further discuss how better indicators for epileptic seizures can be constructed.
为了理解大脑动力学的本质，并开发出用于诊断脑病理学的新方法，最近，许多来自信息论、混沌理论和随机分形理论的复杂性度量被应用于分析 EEG 数据。这些度量对于量化神经动力学的关键概念，包括确定性、随机性、因果关系和相关性，至关重要。因此，寻找和理解这些复杂性度量之间的关系是一个重要的问题。然而，这是一项艰巨的任务，因为信息论、混沌理论和随机分形理论的基础非常不同。为了深入了解这个问题，我们对 EEG 信号的主要复杂性度量进行了全面的比较研究。我们发现，常用复杂性度量随时间的变化要么相似，要么相反。虽然许多这样的关系难以直观地解释，但通过将这些度量与多尺度复杂性度量的特定尺度上的标度相关的 Lyapunov 指数值联系起来，所有这些关系都可以很容易地理解。我们进一步讨论了如何构建更好的癫痫发作指标。
