Tasks with high mental workload often involve higher cognitive functions of the human brain and complex information flow involving multiple brain regions. However, the dynamics of functional connectivity between brain regions during high mental workload have not been well-studied. We use an analysis approach designed to find repeating network states from gamma-band phase locking value networks built from electroencephalograph data collected while participants engaged in tasks with different levels of mental workload. First, we define network states as results of clustering based on the closeness centrality node-level network metric. Second, we found that the transition between network states is not completely random. And, we found significant differences in network state statistics between low and high mental workload. Third, we found significant correlation between features calculated from the network state sequence and behavioral performance. Finally, we use dynamic network features as input to a support vector machine classifier and obtain cross-participant average decoding accuracy of 69.6%. Our methods provide a new perspective for analyzing the dynamics of electroencephalograph signals and have potential application to the decoding of mental workload level.

高脑力负荷的任务往往涉及人脑较高的认知功能和涉及多个大脑区域的复杂信息流。然而，高脑力负荷期间大脑区域之间功能连接的动态尚未得到充分研究。我们使用一种分析方法，旨在从伽马带锁相值网络中找到重复的网络状态，该网络是根据参与者从事不同脑力负荷水平的任务时收集的脑电图数据构建的。首先，我们将网络状态定义为基于紧密中心性节点级网络度量的聚类结果。其次，我们发现网络状态之间的转换并不是完全随机的。而且，我们发现低脑力负荷和高脑力负荷之间的网络状态统计数据存在显着差异。第三，我们发现根据网络状态序列计算出的特征与行为表现之间存在显着相关性。最后，我们使用动态网络特征作为支持向量机分类器的输入，并获得 69.6% 的跨参与者平均解码精度。我们的方法为分析脑电图信号的动态提供了新的视角，并且在解码心理负荷水平方面具有潜在的应用。