脑电信号的统计复杂性和非平凡集体行为
Statistical Complexity and Nontrivial Collective Behavior in Electroencephalografic Signals
摘要：
我们从健康受试者和癫痫患者的脑电图（EEG）信号的全局动力学计算统计复杂性的度量，并且能够建立一个标准来表征两组个体中的集体行为。研究发现，健康人脑电信号的集体动力学具有相对较高的复杂度值，而癫痫患者脑电信号的集体动力学具有相对较高的复杂度值。为了解释这些结果，我们提出了一个模型的网络耦合混沌映射，我们计算的复杂性作为一个参数的函数，并与此措施的出现在系统中的非平凡的集体行为。我们的研究结果表明，非平凡的集体行为的存在与高值的复杂性，从而表明类似的动态集体过程可能发生在人类大脑。我们的研究结果还表明，癫痫是一种与大脑复杂性丧失有关的退行性疾病。
英文摘要：
We calculate a measure of statistical complexity from the global dynamics of electroencephalographic (EEG) signals from healthy subjects and epileptic patients, and are able to stablish a criterion to characterize the collective behavior in both groups of individuals. It is found that the collective dynamics of EEG signals possess relative higher values of complexity for healthy subjects in comparison to that for epileptic patients. To interpret these results, we propose a model of a network of coupled chaotic maps where we calculate the complexity as a function of a parameter and relate this measure with the emergence of nontrivial collective behavior in the system. Our results show that the presence of nontrivial collective behavior is associated to high values of complexity; thus suggesting that similar dynamical collective process may take place in the human brain. Our findings also suggest that epilepsy is a degenerative illness related to the loss of complexity in the brain.
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