A novel method for the identification of synchronization effects in multichannel ECoG with an application to epilepsy

Abstract

In this paper, we present a novel method for the identification of synchronization effects in multichannel electrocorticograms (ECoG). Based on autoregressive modeling, we define a dependency measure termed extrinsic-to-intrinsic power ratio (EIPR) which quantifies directed coupling effects in the time domain. Hereby, a dynamic input channel selection algorithm assures the estimation of the model parameters despite the strong spatial correlation among the high number of involved ECoG channels. We compare EIPR to the partial directed coherence, show its ability to indicate Granger causality and successfully validate a signal model. Applying EIPR to ictal ECoG data of patients suffering from temporal lobe epilepsy allows us to identify the electrodes of the seizure onset zone. The results obtained by the proposed method are in good accordance with the clinical findings.

A Corollary Discharge Circuit in Human Speech

“…We designed a novel directed connectivity analysis framework to study the cortical information flow by fusing techniques that analyze the causal relationships between time-series (22,(28)(29)(30)(31)(32)(33) and unsupervised clustering (orthogonal non-negative matrix factorization ( 24)).…”

A framework for seizure detection using effective connectivity, graph theory, and multi-level modular network

“…Localizing the seizure focus is an important step for seizure surgery in refractory patients. The main purpose of some studies is to detect the propagation pattern across brain areas and identify seizure onset zone (SOZ) [18][19][20][21][22][23][24][25]. These studies show that the brain connectivity patterns can offer practical information about seizure propagation and thus enhance the accuracy of the presurgical evaluation in patients with refractory epilepsy.…”

Identification of Directed Interactions in Kinematic Data during Running

“…With a pairwise analysis and assuming that the process is linear, Gaussian and stationary (Chicharro, 2011 ), the analysis is developed in the time domain from the covariance matrix of the noise terms of the autoregressive (AR) representation of two stochastics processes (Ding et al, 2006 ). Partial directed coherence (PDC), introduced by Baccalá and Sameshima ( 2001 ), is a different approach of Granger causality applied in frequency-domain from Fourier Transform of the multivariate autoregressive (MVAR) coefficients matrix (Graef et al, 2013 ).…”