Localization of Distributed EEG Sources in the Context of Epilepsy: A Simulation Study

Objective: Surface EEG recordings are routinely performed for the diagnosis and management of epilepsy. More particularly, they can help to delineate the brain regions involved in interictal epileptic activity. This is achieved by applying distributed source localization algorithms to the EEG data. Over the last two decades, a multitude of different methods have been proposed. The objective of this paper consists in comparing the performance of eight representative algorithms taking into account recently developed methods. Methods: The performance comparison is based on realistic computer simulations in the context of epileptic source localization. Main findings: All tested algorithms generally permit to identify the source positions, but only some algorithms (STWV-DA, 4-ExSo-MUSIC, SVB-SCCD, cLORETA) also give an indication of the spatial extent of the sources. Localizing deep and mesial sources remains a challenging problem.

[1]  Jean Gotman,et al.  Evaluation of EEG localization methods using realistic simulations of interictal spikes , 2006, NeuroImage.

[2]  Fabrice Bartolomei,et al.  Electric Source Imaging in Frontal Lobe Epilepsy , 2006, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[3]  Laurent Albera,et al.  Localization of extended brain sources from EEG/MEG: The ExSo-MUSIC approach , 2011, NeuroImage.

[4]  J S Ebersole,et al.  Noninvasive Localization of Epileptogenic Foci by EEG Source Modeling , 2000, Epilepsia.

[5]  Fabrice Wendling,et al.  The neuronal sources of EEG: Modeling of simultaneous scalp and intracerebral recordings in epilepsy , 2008, NeuroImage.

[6]  M. Cook,et al.  EEG source localization in focal epilepsy: Where are we now? , 2008, Epilepsia.

[7]  Julia P. Owen,et al.  Robust Bayesian estimation of the location, orientation, and time course of multiple correlated neural sources using MEG , 2010, NeuroImage.

[8]  M. Fuchs,et al.  Smooth reconstruction of cortical sources from EEG or MEG recordings , 1996, NeuroImage.

[9]  R D Pascual-Marqui,et al.  Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. , 2002, Methods and findings in experimental and clinical pharmacology.

[10]  F Wendling,et al.  EEG extended source localization: Tensor-based vs. conventional methods , 2014, NeuroImage.

[11]  R. Pascual-Marqui.  Review of methods for solving the EEG inverse problem , 1999 .

[12]  J S Ebersole,et al.  Magnetoencephalography/Magnetic Source Imaging in the Assessment of Patients with Epilepsy , 1997, Epilepsia.

[13]  Julius P. A. Dewald,et al.  Evaluation of different cortical source localization methods using simulated and experimental EEG data , 2005, NeuroImage.

[14]  Polina Golland,et al.  A distributed spatio-temporal EEG/MEG inverse solver , 2009, NeuroImage.

[15]  Marc Teboulle,et al.  A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems , 2009, SIAM J. Imaging Sci..

[16]  Fabrice Wendling,et al.  A Physiologically Plausible Spatio-Temporal Model for EEG Signals Recorded With Intracerebral Electrodes in Human Partial Epilepsy , 2007, IEEE Transactions on Biomedical Engineering.

[17]  Rémi Gribonval,et al.  Brain-Source Imaging: From sparse to tensor models , 2015, IEEE Signal Processing Magazine.

[18]  Richard M. Leahy,et al.  Electromagnetic brain mapping , 2001, IEEE Signal Process. Mag..

[19]  Bart Vanrumste,et al.  Journal of Neuroengineering and Rehabilitation Open Access Review on Solving the Inverse Problem in Eeg Source Analysis , 2022 .

[20]  M. Murray,et al.  EEG source imaging , 2004, Clinical Neurophysiology.

[21]  David P. Wipf,et al.  A unified Bayesian framework for MEG/EEG source imaging , 2009, NeuroImage.

[22]  Rémi Gribonval,et al.  Fast, variation-based methods for the analysis of extended brain sources , 2014, 2014 22nd European Signal Processing Conference (EUSIPCO).

[23]  E. Somersalo,et al.  Visualization of Magnetoencephalographic Data Using Minimum Current Estimates , 1999, NeuroImage.

[24]  J. Ebersole,et al.  Intracranial EEG Substrates of Scalp EEG Interictal Spikes , 2005, Epilepsia.

[25]  J Gotman,et al.  Reliability of dipole models of epileptic spikes , 1999, Clinical Neurophysiology.

[26]  J. Mixter.  Fast , 2012 .

[27]  A. Dale,et al.  Improved Localizadon of Cortical Activity by Combining EEG and MEG with MRI Cortical Surface Reconstruction: A Linear Approach , 1993, Journal of Cognitive Neuroscience.

[28]  Christophe Grova,et al.  MEG Source Localization of Spatially Extended Generators of Epileptic Activity: Comparing Entropic and Hierarchical Bayesian Approaches , 2013, PloS one.

[29]  Alexandre Gramfort,et al.  Mapping, timing and tracking cortical activations with MEG and EEG: Methods and application to human vision. (Localisation et suivi d'activité fonctionnelle cérébrale en électro et magnétoencéphalographie: Méthodes et applications au système visuel humain) , 2009 .

[30]  Ian T. Paulsen,et al.  Sequences of Two Related Multiple Antibiotic Resistance Virulence Plasmids Sharing a Unique IS26-Related Molecular Signature Isolated from Different Escherichia coli Pathotypes from Different Hosts , 2013, PloS one.

[31]  I. Merlet.  Dipole modeling of interictal and ictal EEG and MEG paroxysms. , 2002, Epileptic disorders : international epilepsy journal with videotape.