Multivariate Synchronization Analysis of Brain Electroencephalography Signals: A Review of Two Methods

Temporal synchronization of neuronal activity plays an important role in various brain functions such as binding, cognition, information processing, and computation. Patients suffering from disorders such as Alzheimer's disease or schizophrenia show abnormality in the synchronization patterns. Electroencephalography (EEG) is a cheap, non-invasive, and easy-to-use method with fine temporal resolution. Modern multichannel EEG data are increasingly being used in brain studies. Traditional approaches for identifying synchronous activity in EEG are through univariate techniques such as power spectral density or bivariate techniques such as coherence. In this paper, we review two methods for synchronization analysis within multivariate time series. One method, denoted by multivariate state-space synchronization-estimator, calculates the generalized synchronization based on the shrinking of the embedding dimension in the state-space. The other method, denoted by multivariate phase synchronization-estimator, considers phase of the signals and calculates the mean coherency within multivariate phases. Their effectiveness is assessed on both simulated data and real EEGs.