Hierarchical Bayesian inference for the EEG inverse problem using realistic FE head models: depth localization and source separation for focal primary currents.

Beitrag in einer Fachzeitschrift


Details zur Publikation

Autorinnen und Autoren: Lucka F, Pursiainen S, Burger M, Wolters CH
Zeitschrift: NeuroImage
Jahr der Veröffentlichung: 2012
Band: 61
Seitenbereich: 1364-82
ISSN: 1053-8119
eISSN: 1095-9572
Sprache: Englisch


Abstract

The estimation of the activity-related ion currents by measuring the induced electromagnetic fields at the head surface is a challenging and severely ill-posed inverse problem. This is especially true in the recovery of brain networks involving deep-lying sources by means of EEG/MEG recordings which is still a challenging task for any inverse method. Recently, hierarchical Bayesian modeling (HBM) emerged as a unifying framework for current density reconstruction (CDR) approaches comprising most established methods as well as offering promising new methods. Our work examines the performance of fully-Bayesian inference methods for HBM for source configurations consisting of few, focal sources when used with realistic, high-resolution finite element (FE) head models. The main foci of interest are the correct depth localization, a well-known source of systematic error of many CDR methods, and the separation of single sources in multiple-source scenarios. Both aspects are very important in the analysis of neurophysiological data and in clinical applications. For these tasks, HBM provides a promising framework and is able to improve upon established CDR methods such as minimum norm estimation (MNE) or sLORETA in many aspects. For challenging multiple-source scenarios where the established methods show crucial errors, promising results are attained. Additionally, we introduce Wasserstein distances as performance measures for the validation of inverse methods in complex source scenarios.


Einrichtungen weiterer Autorinnen und Autoren

Tampere University of Technology
Westfälische Wilhelms-Universität (WWU) Münster


Zitierweisen

APA:
Lucka, F., Pursiainen, S., Burger, M., & Wolters, C.H. (2012). Hierarchical Bayesian inference for the EEG inverse problem using realistic FE head models: depth localization and source separation for focal primary currents. NeuroImage, 61, 1364-82. https://dx.doi.org/10.1016/j.neuroimage.2012.04.017

MLA:
Lucka, Felix, et al. "Hierarchical Bayesian inference for the EEG inverse problem using realistic FE head models: depth localization and source separation for focal primary currents." NeuroImage 61 (2012): 1364-82.

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Zuletzt aktualisiert 2018-28-11 um 16:38