EEG-Based Semantic Vigilance Level Classification Using Directed Connectivity Patterns and Graph Theory Analysis

This paper proposes two novel methods to classify semantic vigilance levels by utilizing EEG directed connectivity patterns with their corresponding graphical network measures. We estimate the directed connectivity using relative wavelet transform entropy (RWTE) and partial directed coherence (PDC) and the graphical network measures by graph theory analysis (GTA) at four frequency bands. The RWTE and PDC quantify the strength and directionality of information flow between EEG nodes. On the other hand, the GTA of the complex network measures summarizes the topological structure of the network. We then evaluate the proposed methods using machine learning classifiers. We carried out an experiment on nine subjects performing semantic vigilance task (Stroop color word test (SCWT)) for approximately 45 minutes. Behaviorally, all subjects demonstrated vigilance decrement as reflected by the significant increase in response time and reduced accuracy. The strength and directionality of information flow in the connectivity network by RWTE/PDC and the GTA measures significantly decrease with vigilance decrement, p<; 0.05. The classification results show that the proposed methods outperform other related and competitive methods available in the literature and achieve 100% accuracy in subject-dependent and above 89% in subject-independent level in each of the four frequency bands. The overall results indicate that the proposed methods of directed connectivity patterns and GTA provide a complementary aspect of functional connectivity. Our study suggests directed functional connectivity with GTA as informative features and highlight Support Vector Machine as the suitable classifier for classifying semantic vigilance levels.