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.