Mental fatigue has serious negative impacts on the brain cognitive functions and has been widely explored by the means of brain functional networks with the neuroimaging technique of electroencephalogram (EEG). Recently, several researchers reported that brain functional network constructed from EEG signals has fractal feature, raising an important question: what are the effects of mental fatigue on the fractal dimension of brain functional network? In the present study, the EEG data of alpha1 rhythm (8-10 Hz) at task state obtained by a mental fatigue model were chosen to construct brain functional networks. A modified greedy colouring algorithm was proposed for fractal dimension calculation in both binary and weighted brain functional networks. The results indicate that brain functional networks still maintain fractal structures even when the brain is at fatigue state; fractal dimension presented an increasing trend along with the deepening of mental fatigue fractal dimension of the weighted network was more sensitive to mental fatigue than that of binary network. Our current results suggested that mental fatigue has great regular impacts on the fractal dimension in both binary and weighted brain functional networks.

中文翻译：

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精神疲劳对大脑功能网络的分形维度有很大影响


精神疲劳对大脑认知功能有严重的负面影响，通过脑电图（EEG）神经影像技术的脑功能网络手段已被广泛研究。最近，多名研究人员报道，由脑电信号构建的脑功能网络具有分形特征，这提出了一个重要问题：精神疲劳对脑功能网络的分形维数有何影响？本研究选择精神疲劳模型获得的任务状态下α1节律（8-10 Hz）的脑电数据来构建大脑功能网络。提出了一种改进的贪婪着色算法，用于二元和加权脑功能网络中的分形维数计算。结果表明，即使大脑处于疲劳状态，大脑功能网络仍然保持分形结构；随着精神疲劳的加深，分形维数呈现增加的趋势，加权网络的分形维数比二值网络对精神疲劳更敏感。我们目前的结果表明，精神疲劳对二元和加权大脑功能网络的分形维数都有很大的规律性影响。