The visual interpretation of intracranial EEG (iEEG) is used clinically to map the regions of seizure onset targeted for resection during epilepsy surgery. Still, visual iEEG analysis is labor-intensive and biased due to interpreter dependency. Multivariate parametric functional-connectivity measures using adaptive autoregressive (AR) modeling of the iEEG signal based on the Kalman filter algorithm have been used successfully to localize the electrographic seizure onsets. Due to their high

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... tational cost, these methods have been applied to a limited number of iEEG time-series (<60). The aim of this study was to test two Kalman filter implementations, a well-known multivariate adaptive AR model (Arnold et al. 1998 ) and a simplified, computationally efficient derivation of it, for their potential application to connectivity analysis of high-dimensional (up to 192 channels) iEEG data. When used together with a multivariate connectivity estimator, the partial directed coherence, the two AR models were compared in seizure simulations for their ability to reconstitute the designed seizure signal connections from noisy data. Next, the onset of a 113-channel iEEG seizure recorded in a patient rendered seizure-free after surgery was estimated with the out-degree, a graph-theory index of outward directed connectivity. Simulation results indicated high mapping accuracy for the two models in the presence of low-to-moderate noise cross-correlation. Accordingly, both AR models correctly mapped the real seizure onset to the resection volume. This study supports the possibility of conducting fully data-driven multivariate connectivity estimations on high-dimensional iEEG datasets using the Kalman filter approach.