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Investigate intracranial EEG with conditional Granger causality and PCA

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机构： [1]School of Applied Mathematics, University of Electronic Science and Technology of China, Chengdu 610054, China [2]Xuanwu Hospital, Capital Medical University, Beijing 100069, China [3]Key Laboratory for NeuroInformation of Ministry of Education, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China

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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. © 2010 IEEE.

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第一作者机构： [1]School of Applied Mathematics, University of Electronic Science and Technology of China, Chengdu 610054, China

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通讯机构： [1]School of Applied Mathematics, University of Electronic Science and Technology of China, Chengdu 610054, China [3]Key Laboratory for NeuroInformation of Ministry of Education, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China

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