Depicting the relationship between brain cognitive state and task difficulty level constitutes a challenging problem of significant importance. In order to probe it, we design an experiment to gather EEG data from mental arithmetic task under different difficulty levels. We construct brain complex networks using a complex network method and information entropy theory. We then employ weighted clustering coefficient to characterize the networks generated from different brain cognitive states. The results show that with the increase in task difficulty level, the mean weighted clustering coefficients show a decrease. This is due to the lack of coordination of brain activity and the low efficiency of the network organization caused by the increase in task difficulty. In addition, we calculate the permutation entropy from the signals of each channel EEG signals to support the findings from our network analysis. These findings render our method particularly useful for depicting the relationship between brain cognitive state and difficulty level.

中文翻译：

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用于解码大脑认知状态的实验脑电信号的复杂网络分析

描述大脑认知状态和任务难度水平之间的关系构成了一个具有重要意义的具有挑战性的问题。为了探讨这一问题，我们设计了一个实验，从不同难度级别的心算任务中收集脑电数据。我们使用复杂网络方法和信息熵理论构建大脑复杂网络。然后我们使用加权聚类系数来表征从不同大脑认知状态生成的网络。结果表明，随着任务难度级别的增加，平均加权聚类系数呈下降趋势。这是由于任务难度增加导致大脑活动缺乏协调性和网络组织效率低下所致。此外，我们根据每个通道 EEG 信号的信号计算排列熵，以支持我们网络分析的结果。这些发现使我们的方法对于描述大脑认知状态和难度水平之间的关系特别有用。