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EEG-Based Semantic Vigilance Level Classification using Directed Connectivity Patterns and Graph Theory Analysis
2020
IEEE Access
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
doi:10.1109/access.2020.3004504
fatcat:aqoh532grbhmpa6s4yi3za3xze