Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-6471
Main Title: Identifying causal networks of neuronal sources from EEG/MEG data with the phase slope index: a simulation study
Author(s): Ewald, Arne
Avarvand, Forooz Shahbazi
Nolte, Guido
Type: Article
Language Code: en
Abstract: The investigation of functional neuronal synchronization has recently become a growing field of research. With high temporal resolution, electroencephalography and magnetoencephalography are well-suited measurement techniques to identify networks of interacting sources underlying the recorded data. The analysis of the data in terms of effective connectivity, nevertheless, contains intrinsic issues such as the problem of volume conduction and the non-uniqueness of the inverse solution. Here, we briefly introduce a series of existing methods assessing these problems. To determine the locations of interacting brain sources robust to volume conduction, all computations are solely based on the imaginary part of the cross-spectrum as a trustworthy source of information. Furthermore, we demonstrate the feasibility of estimating causal relationships of systems of neuronal sources with the phase slope index in realistically simulated data. Finally, advantages and drawbacks of the applied methodology are highlighted and discussed.
URI: https://depositonce.tu-berlin.de//handle/11303/7196
http://dx.doi.org/10.14279/depositonce-6471
Issue Date: 2013
Date Available: 28-Nov-2017
DDC Class: 610 Medizin, Gesundheit
Subject(s): effective connectivity
electroencephalography
imaginary part of coherency
magnetoencephalography
source localization
volume conduction
Usage rights: Terms of German Copyright Law
Journal Title: Biomedical engineering = Biomedizinische Technik
Publisher: De Gruyter
Publisher Place: Berlin [u.a.]
Volume: 58
Issue: 2
Publisher DOI: 10.1515/bmt-2012-0028
Page Start: 165
Page End: 178
EISSN: 0013-5585
ISSN: 1862-278X
Notes: Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.
This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.
Appears in Collections:Fachgebiet Maschinelles Lernen » Publications

Files in This Item:
File Description SizeFormat 
bmt-2012-0028.pdf4.09 MBAdobe PDFThumbnail
View/Open


Items in DepositOnce are protected by copyright, with all rights reserved, unless otherwise indicated.