Multidimensional Directed Phase Analysis for Basic Rhythm of EEG

Abstract

In this paper, multidimensional directed phase analysis is proposed as a means of causality analysis for multiple time series, is proposed. The multidimensional directed phase is a phase with causality, which is associated with multidimensional directed coherence. Using multidimensional directed phase analysis, direction and time of a signal flow among multidimensional time series can be estimated. In two simulations, artificial time series have been analyzed by the new method to confirm its characteristics. In first simulation, accuracy of estimation of signal flow time has been investigated. Second, a complex signal flow pattern has been analyzed. Next, the multidimensional directed phase analysis and the multidimensional directed coherence analysis have been applied to EEG data of normal volunteer. As a result, we have got information differ from common learning which is the difference between the phase of a frontal alpha waves and an occipital is π.