Objective: Previous studies have already shown that electroencephalography (EEG) brain network (BN) can reflect the health status of individuals. However, novel methods are still needed for BN analysis. Therefore, in this study, BNs were constructed based on stable and unstable EEG components, and these may be implemented for disease diagnosis. Methods: Parkinson's disease (PD) was used as an example to illustrate this method. First, EEG signals were decomposed into dynamic modes (DMs). Each DM contains one eigenvalue that can determine not only the stability of that mode, but also its corresponding oscillatory frequency. Second, the stable and unstable components of EEG signals in each frequency band (delta, theta, alpha and beta) were calculated, which are based on the stable and unstable DMs within each respective frequency band. Third, newly developed BNs were constructed, including stable brain network (SBN), unstable brain network (UBN) and inter-connected brain network (IBN). Finally, their topological attributes were extracted in order to differentiate between PD patients and healthy controls (HC). Furthermore, topological attributes were also derived from traditional brain network (TBN) for comparison. Results: Most topological attributes of SBN, UBN and IBN can significantly differentiate between PD patients and HC ( $p$ value $< $ 0.05). Furthermore, the area under the curve (AUC), precision and recall values of SBN analysis are all significantly higher than TBN. Conclusion: We proposed a new perspective on EEG BN analysis. Significance: These newly developed BNs not only have biological significance, but also could be widely applied in most medical and engineering fields.

中文翻译：

---

通过稳定和不稳定的 EEG 组件进行脑网络分析

目的：先前的研究已经表明，脑电图（EEG）脑网络（BN）可以反映个体的健康状况。然而，BN分析仍然需要新的方法。因此，在本研究中，BNs 是基于稳定和不稳定的 EEG 成分构建的，这些可能用于疾病诊断。方法：以帕金森病（PD）为例来说明该方法。首先，EEG 信号被分解为动态模式 (DM)。每个 DM 包含一个特征值，不仅可以确定该模式的稳定性，还可以确定其相应的振荡频率。其次，计算每个频带（delta、theta、alpha 和 beta）中 EEG 信号的稳定和不稳定分量，这些分量基于每个相应频带内的稳定和不稳定 DM。第三，构建了新开发的BN，包括稳定脑网络（SBN）、不稳定脑网络（UBN）和互连脑网络（IBN）。最后，提取它们的拓扑属性以区分 PD 患者和健康对照 (HC)。此外，拓扑属性也来自传统的大脑网络（TBN）以进行比较。结果：SBN、UBN 和 IBN 的大多数拓扑属性可以显着区分 PD 患者和 HC（拓扑属性也来自传统大脑网络（TBN）以进行比较。结果：SBN、UBN 和 IBN 的大多数拓扑属性可以显着区分 PD 患者和 HC（拓扑属性也来自传统大脑网络（TBN）以进行比较。结果：SBN、UBN 和 IBN 的大多数拓扑属性可以显着区分 PD 患者和 HC（ $p$ 价值 $< $0.05）。此外，SBN 分析的曲线下面积（AUC）、精度和召回值都显着高于 TBN。结论：我们提出了 EEG BN 分析的新视角。意义：这些新开发的BN不仅具有生物学意义，而且可以广泛应用于大多数医学和工程领域。