Department of Computer Science, Humboldt-Universität zu Berlin, Rudower Chaussee 25, 12489 Berlin, Germany; Unter den Linden 6, 10099 Berlin, Germany.
Department of Computer Science, Humboldt-Universität zu Berlin, Rudower Chaussee 25, 12489 Berlin, Germany; Unter den Linden 6, 10099 Berlin, Germany.
J Neurosci Methods. 2018 Sep 1;307:138-148. doi: 10.1016/j.jneumeth.2018.06.010. Epub 2018 Jun 21.
Although acquisition techniques have improved tremendously, the neuroscientific understanding of complex cognitive phenomena is still incomplete. One of the reasons for this shortcoming may be the lack of sophisticated signal processing methods. Complex cognitive phenomena usually involve various mental subprocesses whose temporal occurrence varies from trial to trial. Mostly, these mental subprocesses require large-scale integration processes between multiple brain areas that are most likely mediated by complex, non-linear phase coupling mechanisms. Consequently, a spatiotemporal analysis of complex, multivariate phase synchronization patterns on a single trial basis is necessary.
This paper introduces the HEURECA method (How to Evaluate and Uncover Recurring EEG Coupling Arrangements) that enables the dynamic detection of distinguishable multivariate functional connectivity states in the electroencephalogram. HEURECA adaptively divides a trial into segments of quasi-stable phase coupling topographies and assigns similar topographies to the same synchrostate cluster.
HEURECA is evaluated by means of simulated data. The results show that it reliably reconstructs a time series of recurring phase coupling topographies and successfully gathers them into clusters of interpretable neural synchrostates. The advantages and unique features of HEURECA are further illustrated by investigating the popular complex cognitive phenomenon insight.
Unlike existing methods, HEURECA detects complex phase relationships between more than two signals and is applicable to single trials.
Since HEURECA is applicable to all kinds of circular data, it not only provides new insights into insight, but also into a variety of other phenomena in neuroscience, physics or other scientific fields.
尽管采集技术已经有了巨大的进步，但对于复杂认知现象的神经科学理解仍然不完整。造成这种缺陷的原因之一可能是缺乏复杂的信号处理方法。复杂认知现象通常涉及各种心理子过程，其时间发生在每次试验中都有所不同。这些心理子过程大多需要多个大脑区域之间的大规模整合过程，而这些过程很可能是由复杂的非线性相位耦合机制介导的。因此，有必要在单次试验的基础上对复杂的、多变量的相位同步模式进行时空分析。
本文介绍了 HEURECA 方法（如何评估和揭示 EEG 耦合排列的重复），该方法能够动态检测脑电图中可区分的多变量功能连接状态。HEURECA 自适应地将试验划分为准稳定相位耦合拓扑的片段，并将相似的拓扑分配给相同的同步状态簇。
通过模拟数据对 HEURECA 进行了评估。结果表明，它能够可靠地重建重复的相位耦合拓扑时间序列，并成功地将它们收集到可解释的神经同步状态簇中。通过研究流行的复杂认知现象洞察力，进一步说明了 HEURECA 的优势和独特功能。
与现有方法不同，HEURECA 检测两个以上信号之间的复杂相位关系，并且适用于单次试验。
由于 HEURECA 适用于所有类型的循环数据，它不仅为洞察力提供了新的见解，而且为神经科学、物理学或其他科学领域的各种其他现象提供了新的见解。
