MULTICHANNEL EEG ANALYSIS BASED ON MULTI-SCALE MULTI-INFORMATION

Functional connectivity has been widely used to reveal the dependencies between signals in complex networks such as neural networks observed from electroencephalogram (EEG) data. The interactions among neural oscillations are known to be nonlinear and non-stationary. Classical measures for quantifying these interactions only capture the linear relationships, are mostly defined in either the time or frequency domain, and are limited to pairwise relationships. In this paper, we propose a multi-scale multi-information measure to quantify the interdependencies among multiple variables in both time and frequency domains. Multivariate empirical mode decomposition (MEMD) is employed to decompose signals into different frequency bands and multi-information is used to quantify the dependencies between these signals across time and frequency. The proposed measure is applied to both simulated data and EEG data to evaluate its effectiveness.