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

M Rubega, M Carboni, M Seeber, D Pascucci, S Tourbier, G Toscano, P Van Mierlo, P Hagmann, G Plomp, S Vulliemoz, C M Michel

Research output: Contribution to journalArticle

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.

Original languageEnglish
Pages (from-to)704-719
Number of pages16
JournalBrain Topography
Volume32
Issue number4
DOIs
Publication statusPublished - Jul 2019
Externally publishedYes

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    Rubega, M., Carboni, M., Seeber, M., Pascucci, D., Tourbier, S., Toscano, G., Van Mierlo, P., Hagmann, P., Plomp, G., Vulliemoz, S., & Michel, C. M. (2019). Estimating EEG Source Dipole Orientation Based on Singular-value Decomposition for Connectivity Analysis. Brain Topography, 32(4), 704-719. https://doi.org/10.1007/s10548-018-0691-2