Brain Network Analysis by Stable and Unstable EEG Components.

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.

Assessment of Balance Control Subsystems by Artificial Intelligence.

Recent studies have shown that balance performance assessment based on artificial intelligence (AI) is feasible. However, balance control is very complex and requires different subsystems to participate, which have not been evaluated individually yet. Furthermore, these studies only classified individual's balance performance across limited grades. Therefore, in this study we attempted to implement AI to precisely evaluate different types of balance control subsystems (BCSes). First, a total of 224 commonly used and newly developed features were extracted from the center of pressure (CoP) data for each participant, respectively. Then, regressors were employed in order to map these features to the evaluation scores given by physical therapists, which include the total score in Mini-Balance-Evaluation-Systems-Tests (Mini-BESTest) and its sub-scores on BCSes, namely anticipatory postural adjustments (APA), reactive postural control (RPC), sensory orientation (SO), and dynamic gait (DG). Their scoring ranges should be 0-28, 0-6, 0-6, 0-6, and 0-10, respectively. The results show that their minimum mean absolute errors from AI estimation reach up to 2.658, 0.827, 0.970, 0.642, and 0.98, respectively. In sum, our study is a preliminary study for assessing BCSes based on AI, which shows its possibility to be used in the clinics in the future.