Time Variant Causality Model Applied in Brain Connectivity Network Based on Event Related Potential

Granger causality model mostly used to find the interaction between different time series are more and more applied to natural neural network at present. Brain connectivity network that could imply interaction and coordination between different brain regions is a focused research of brain function. Usually synchronization and correlation are used to reveal the connectivity network based on event-related potential (ERP) signals. However, these methods lack the further information such as direction of the connectivity network. In this paper, we performed an approach to detect the direction by Granger causality model. Considering the non-stationary of ERP data, we used traditional recursive least square (RLS) algorithm to calculate time variant Granger causality. In particular, we extended the method on the significance of causality measures in order to make results more reasonable. These approaches were applied to the classic Stroop cognitive experiment to establish the causality network related to attention process mechanism.

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