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  Spatio-temporal regularization in linear distributed source reconstruction from EEG/MEG: A critical evaluation

Dannhauer, M., Lämmel, E., Wolters, C., & Knösche, T. R. (2013). Spatio-temporal regularization in linear distributed source reconstruction from EEG/MEG: A critical evaluation. Brain Topography, 26(2), 229-246. doi:10.1007/s10548-012-0263-9.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0014-6997-D Version Permalink: http://hdl.handle.net/21.11116/0000-0003-8706-E
Genre: Journal Article

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 Creators:
Dannhauer, Moritz1, Author              
Lämmel, Eric1, Author
Wolters, Carsten2, Author
Knösche, Thomas R.1, Author              
Affiliations:
1Methods and Development Unit Cortical Networks and Cognitive Functions, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634557              
2Institute for Biomagnetism and Biosignal Analysis, Münster University, Germany, ou_persistent22              

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Free keywords: Source localization; Spatio-temporal regularization; sLORETA; EEG
 Abstract: The high temporal resolution of EEG/MEG data offers a way to improve source reconstruction estimates which provide insight into the spatio-temporal involvement of neuronal sources in the human brain. In this work, we investigated the performance of spatio-temporal regularization (STR) in a current density approach using a systematic comparison to simple ad hoc or post hoc filtering of the data or of the reconstructed current density, respectively. For the used STR approach we implemented a frequency-specific constraint to penalize solutions outside a narrow frequency band of interest. The widely used sLORETA algorithm was adapted for STR and generally used for source reconstruction. STR and filtering approaches were evaluated with respect to spatial localization error and spatial dispersion, as well as to correlation of original and reconstructed source time courses in single source and two source scenarios with fixed source locations and oscillating source waveforms. We used extensive computer simulations and tested all algorithms with different parameter settings (noise levels and regularization parameters) for EEG data. To verify our results, we also used data from MEG phantom measurements. For the investigated scenarios, we did not find any evidence that STR-based methods outperform purely spatial algorithms applied to temporally filtered data. Furthermore, the results show very clearly that the performance of STR depends very much on the choice of regularization parameters.

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Language(s): eng - English
 Dates: 2012-06-252012-10-132012-10-312013-04
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1007/s10548-012-0263-9
PMID: 23112100
Other: Epub 2012
 Degree: -

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Title: Brain Topography
  Other : Brain Topogr.
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: New York, NY : Human Sciences Press
Pages: - Volume / Issue: 26 (2) Sequence Number: - Start / End Page: 229 - 246 Identifier: ISSN: 0896-0267
CoNE: https://pure.mpg.de/cone/journals/resource/954925560559