A Temporal Multi-View Fuzzy Classifier for Fusion Identification on Epileptic Brain Network

Abstract

Brain networks are commonly used to identify cognitive neurobehavioral and brain conscious disorders. Most of the studies on state networks focus on the characterization and expression of resting-state brain networks, but there are few studies on dynamic brain networks. In fact, the analysis of dynamic brain networks can find more valuable information, because it can dynamically depict the dynamic characteristics of brain networks from the time dimension. However, too much consideration of the expression of dynamic networks will naturally hide many of its static characteristics. Therefore, this study proposes a tense-based multi-view fusion fuzzy learning model (TM-FL) to identify dynamic brain networks. TM-FL relearns the features of adjacent networks in high-dimensional space, mining the dynamic features between adjacent brain networks. Best of all, TM-FL can also capture resting information in the brain network at different times in the source space. The fuzzy TSK-based TM-FL also possesses the strong interpretability inherent in fuzzy systems. In this study, a fusion mechanism is designed to effectively integrate decision support under dynamic and static features, and enhance the classification and generalization performance of the proposed fuzzy classification model. For the proposed fusion fuzzy mechanism, Simon entropy is cleverly set as the regular term of the objective function, which not only constrains the decision weight, but also avoids the embarrassment of making mistakes in a leading decision. Experimental results show that TM-FL has good classification, generalization and interpretable capabilities in recognition of multi-channel epileptic electroencephalographic (EEG) signals.