Estimation of genuine and random synchronization in multivariate neural series.

Synchronization is an important mechanism that helps in understanding information processing in a normal or abnormal brain. In this paper, we propose a new method to estimate the genuine and random synchronization indexes in multivariate neural series, denoted as GSI (genuine synchronization index) and RSI (random synchronization index), by means of a correlation matrix analysis and surrogate technique. The performance of the method is evaluated by using a multi-channel neural mass model (MNMM), including the effects of different coupling coefficients, signal to noise ratios (SNRs) and time-window widths on the estimation of the GSI and RSI. Results show that the GSI and the RSI are superior in description of the synchronization in multivariate neural series compared to the S-estimator. Furthermore, the proposed method is applied to analyze a 21-channel scalp electroencephalographic recording of a 35 year-old male who suffers from mesial temporal lobe epilepsy. The GSI and the RSI at different frequency bands during the epileptic seizure are estimated. The present results could be helpful for us to understand the synchronization mechanism of epileptic seizures.