发表机构
School of Biomedical Sciences, University of New South Wales (UNSW); Department of Neurology, Christian-Albrechts University（新南威尔士大学生物科学学院; 基尔大学神经病学系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究探讨元准则在无簇动力学系统及静息态脑电中的表现，发现其给出虚假聚类数且质心落入吸引子区域，表明数据可能形成单一连通结构而非簇，故应谨慎使用元准则。
AI 中文摘要
微状态分析始于聚类，而元准则是一种寻找最优聚类数的启发式方法。我们探讨以下问题：（i）对于不形成簇的动力学系统，元准则会给出什么聚类数；（ii）质心是否落入随机吸引子区域；（iii）脑电图地形图在传感器空间中形成簇还是单一连通结构？我们发现：（i）元准则在不同吸引子几何结构上以高置信度建议虚假的最优聚类数（4-8）；（ii）聚类质心可靠地落入相同的吸引子区域；（iii）对动力学系统和静息态脑电图的拓扑分析表明，所有系统在其各自的相空间中形成单一连通结构，而非簇。我们得出结论：应谨慎使用元准则，其结果不应被视为代表真实情况。偏离元准则的聚类数不应被丢弃。一个更深远的含义是，静息态脑电数据中簇存在的证据仍然缺乏。微状态聚类可能对应于对单一连通结构的分割。
英文摘要
Microstate analysis starts with clustering and the meta-criterion is a heuristic to find an optimum cluster number. We address the following questions: (i) what number is found for non-cluster-forming dynamical systems, (ii) do centroids fall in random attractor regions, (iii) do EEG topographies form clusters or a single connected structure in sensor space? We find that (i) the meta-criterion suggests spurious optimal cluster numbers (4-8) with high confidence on different attractor geometries, (ii) cluster centroids reliably fall in the same attractor regions, (iii) topological analysis of dynamical systems and resting-state EEG suggests that all form a single connected structure in their respective phase space, not clusters. We conclude that the meta-criterion should be used with caution and its results should not be taken as representing a ground truth. Cluster numbers deviating from the meta-criterion should not be discarded. A more far-reaching implication is that evidence for the existence of clusters in resting-state EEG data is still lacking. Microstate clustering may correspond to the partitioning of a single connected structure.
