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Estimating EEG Source Dipole Orientation Based on Singular-value Decomposition for Connectivity Analysis

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Abstract

In the last decade, the use of high-density electrode arrays for EEG recordings combined with the improvements of source reconstruction algorithms has allowed the investigation of brain networks dynamics at a sub-second scale. One powerful tool for investigating large-scale functional brain networks with EEG is time-varying effective connectivity applied to source signals obtained from electric source imaging. Due to computational and interpretation limitations, the brain is usually parcelled into a limited number of regions of interests (ROIs) before computing EEG connectivity. One specific need and still open problem is how to represent the time- and frequency-content carried by hundreds of dipoles with diverging orientation in each ROI with one unique representative time-series. The main aim of this paper is to provide a method to compute a signal that explains most of the variability of the data contained in each ROI before computing, for instance, time-varying connectivity. As the representative time-series for a ROI, we propose to use the first singular vector computed by a singular-value decomposition of all dipoles belonging to the same ROI. We applied this method to two real datasets (visual evoked potentials and epileptic spikes) and evaluated the time-course and the frequency content of the obtained signals. For each ROI, both the time-course and the frequency content of the proposed method reflected the expected time-course and the scalp-EEG frequency content, representing most of the variability of the sources (~ 80%) and improving connectivity results in comparison to other procedures used so far. We also confirm these results in a simulated dataset with a known ground truth.

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Acknowledgements

The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper.

Funding

This study was supported by the Swiss National Science Foundation (Grant No. CRSII5-170873 to PH, PvM, GP, SV and CMM; Grant No. 320030_159705 to CMM; Grant No. PP00P1_157420 to GP; No. 320030-169198 to SV), by the National Centre of Competence in Research (NCCR) “SYNAPSY—The Synaptic Basis of Mental Diseases” (NCCR Synapsy Grant No. 51NF40-158776 to PH and CMM), by the Foundation Gertrude Von Meissner (to SV), and by the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement (Grant No. 660230 to PvM).

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Authors and Affiliations

  1. Functional Brain Mapping Lab, Department of Fundamental Neurosciences, University of Geneva, Campus Biotech, Chemin des Mines 9, 1202, Geneva, Switzerland

    M. Rubega, M. Carboni, M. Seeber & C. M. Michel

  2. EEG and Epilepsy Unit, University Hospital of Geneva, Geneva, Switzerland

    M. Carboni, G. Toscano, P. Van Mierlo & S. Vulliemoz

  3. Perceptual Networks Group, Department of Psychology, University of Fribourg, Fribourg, Switzerland

    D. Pascucci & G. Plomp

  4. Department of Radiology, University Hospital of Lausanne, Lausanne, Switzerland

    S. Tourbier & P. Hagmann

  5. Unit of Sleep Medicine and Epilepsy, C. Mondino National Neurological Institute, Pavia, Italy

    G. Toscano

  6. Medical Image and Signal Processing Group, Department of Electronics and Information Systems, Ghent University, Ghent, Belgium

    P. Van Mierlo

  7. Lemanic Biomedical Imaging Centre (CIBM), Lausanne, Geneva, Switzerland

    C. M. Michel

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  1. M. Rubega
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Correspondence to M. Rubega.

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Handling Editor: Jorge Javier Riera.

This is one of several papers published together in Brain Topography on the “Special Issue: Controversies in EEG Source Analysis”.

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Rubega, M., Carboni, M., Seeber, M. et al. Estimating EEG Source Dipole Orientation Based on Singular-value Decomposition for Connectivity Analysis. Brain Topogr 32, 704–719 (2019). https://doi.org/10.1007/s10548-018-0691-2

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