It has been shown that brain functional networks constructed from electroencephalographic signals (EEG) continuously change topology as brain fatigue increases, and extracting the topological properties of the network can characterize the degree of brain fatigue. However, the traditional brain function network construction process often selects only the amplitude or phase components of the signal to measure the relationship between brain regions, and the use of a single component of the signal to construct a brain function network for analysis is rather one-sided. Therefore, we propose a method of functional synchronization analysis of brain regions. This method takes the EEG signal based on empirical modal decomposition (EMD) to obtain multiple intrinsic modal components (IMF) and inputs them into the Hilbert transform to obtain the instantaneous amplitude, and then calculates the amplitude locking value (ALV) to measure the synchronization relationship between all pairs of channels. The topological properties of the brain functional network are extracted to classify awake and fatigue states. The brain functional network is constructed based on the adjacency matrix of each waveform obtained from the ALV between all pairs of channels to realize the synchronization analysis between brain regions. Moreover, we achieved a satisfactory classification accuracy (82.84%) using the discriminative connection features in the Alpha band. In this study, we analyzed the functional network of ALV brain in fatigue and awake state, and the results showed that the connections between brain regions in fatigue state were significantly increased, and the connections between brain regions in the awake state were significantly decreased, and the information interaction between brain regions was more orderly and efficient.

研究表明，随着脑疲劳程度的增加，由脑电图信号（EEG）构建的脑功能网络不断改变拓扑结构，提取网络的拓扑特性可以表征脑疲劳程度。然而，传统的脑功能网络构建过程往往仅选择信号的幅度或相位分量来衡量脑区域之间的关系，而利用信号的单个分量来构建脑功能网络进行分析是相当单一的——一边。因此，我们提出了一种大脑区域功能同步分析的方法。该方法将脑电信号基于经验模态分解（EMD）获得多个本征模态分量（IMF），输入希尔伯特变换得到瞬时幅度，然后计算幅度锁定值（ALV）来衡量同步性所有通道对之间的关​​系。提取大脑功能网络的拓扑特性以对清醒和疲劳状态进行分类。基于所有对通道之间的ALV获得的每个波形的邻接矩阵构建脑功能网络，实现脑区域之间的同步分析。此外，我们使用 Alpha 频带中的判别连接特征实现了令人满意的分类精度（82.84％）。 本研究对疲劳和清醒状态下ALV大脑的功能网络进行了分析，结果显示，疲劳状态下脑区之间的连接显着增加，清醒状态下脑区之间的连接显着减少，并且大脑区域之间的信息交互更加有序和高效。