A large-scale network provides a high hierarchical level for understanding the adaptive adjustment of the human brain during cognition processes. Since high spatial resolution is required, most of the related works are based on functional magnetic resonance imaging (fMRI); however, fMRI lacks the temporal information that is important in investigating the high cognition processes. Although combining electroencephalography (EEG) inverse solution and independent component analysis (ICA), researchers detected large-scale functional subnetworks recently, few researchers focus on the unreasonable negative activation, which is biased from the nonnegative electrical source activations in the brain. In this study, considering the favorable nonnegative property of Bayesian nonnegative matrix factorization (Bayesian NMF) and combining EEG source imaging, we developed a robust approach for EEG large-scale network construction and applied it to two independent real EEG datasets (i.e., decision-making and P300). Eight and nine best-fit networks, including such important subnetworks as the somatosensory-motor network (SMN), the default mode network (DMN), etc., were successfully identified for decision-making and P300, respectively. Compared to the networks acquired with ICA, these networks not only lacked confusing negative activations but also showed clear spatial distributions that are compatible with specific brain function. Based on the constructed large-scale network, we further probed that the self-referential network (SRN), the primary visual network (PVN), and the visual network (VN) demonstrated different interaction patterns with other networks between different responses in decision-making. Our results confirm the possibility of probing the neural mechanisms of high cognition processes at a very high temporal and spatial resolution level.

大型网络为理解认知过程中人脑的适应性调节提供了较高的层次性。由于需要较高的空间分辨率，因此大多数相关工作都基于功能磁共振成像（fMRI）。然而，功能磁共振成像缺乏在研究高认知过程中重要的时间信息。尽管将脑电图（EEG）逆解和独立成分分析（ICA）结合使用，但研究人员最近发现了大规模的功能子网，但很少有研究人员专注于不合理的负激活，这是由于大脑中非负电源激活所引起的。在这项研究中，考虑到贝叶斯非负矩阵分解（贝叶斯NMF）的有利非负性质，并结合EEG源成像，我们开发了一种健壮的方法来进行EEG大规模网络建设，并将其应用于两个独立的真实EEG数据集（即决策和P300）。分别成功地确定了八个和九个最合适的网络，包括体感运动网络（SMN），默认模式网络（DMN）等重要的子网，用于决策和P300。与使用ICA获得的网络相比，这些网络不仅缺少令人困惑的负激活，而且还显示出与特定脑功能兼容的清晰空间分布。在构建的大型网络的基础上，我们进一步探讨了自参考网络（SRN），主视觉网络（PVN），视觉网络（VN）在决策过程中表现出与其他网络不同的交互模式。我们的结果证实了在非常高的时空分辨率水平上探索高认知过程的神经机制的可能性。