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Spatio-temporal Regularization in Linear Distributed Source Reconstruction from EEG/MEG: A Critical Evaluation

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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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Notes

  1. As for the frequency ranges normally considered in EEG and MEG, the temporal derivatives in the Maxwell equations can be ignored (quasistatic assumption; Plonsey and Heppner 1967), these properties amount to the spatial distribution of the electrical conductivity.

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Acknowledgments

This work was kindly supported by the Deutsche Forschungsgemeinschaft (contract Grant Numbers KN 588/2-1,4-1 and WO 1425/1-1,3-1). None of the authors has any conflict of interest, financial or otherwise, related to the submitted.

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Authors and Affiliations

  1. Max Planck Institute for Human Cognitive and Brain Sciences, P.O. Box 500355, 04303, Leipzig, Germany

    Moritz Dannhauer, Eric Lämmel & Thomas R. Knösche

  2. Institute for Biomagnetism and Biosignalanalysis, University of Münster, 48149, Münster, Germany

    Carsten H. Wolters

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  1. Moritz Dannhauer
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  2. Eric Lämmel
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  3. Carsten H. Wolters
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  4. Thomas R. Knösche
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Correspondence to Thomas R. Knösche.

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Dannhauer, M., Lämmel, E., Wolters, C.H. et al. Spatio-temporal Regularization in Linear Distributed Source Reconstruction from EEG/MEG: A Critical Evaluation. Brain Topogr 26, 229–246 (2013). https://doi.org/10.1007/s10548-012-0263-9

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  • DOI: https://doi.org/10.1007/s10548-012-0263-9

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