Investigate intracranial EEG with conditional granger causality and PCA

It's an important basic work to locate the propagation pathways of seizure, as it could bring a crucial effect to guide the clinical practice. This article develops a method for computing effective connectivity on intracranial electroencephalographic (IEEG) data, based on multivariate autoregressive model. We use Principal Component Analysis (PCA) technique on the condition variate sets while calculate the conditional Granger causality (cGC), in order to overcome the redundancy on the condition variate sets. We confirm the proposed approach is robust and feasible application on the simulation data which the condition variates are redundant for executing cGC analysis. The applicability and usefulness of this technique are illustrated using intracranial EEG data from one patient with epilepsy.