Estimating EEG Source Dipole Orientation Based on Singular-value Decomposition for Connectivity Analysis. Rubega, M., Carboni, M., Seeber, M., Pascucci, D., Tourbier, S., Toscano, G., Van Mierlo, P., Hagmann, P., Plomp, G., Vulliemoz, S., & Michel, C. M. Brain Topography, Dec, 2018. ![Estimating EEG Source Dipole Orientation Based on Singular-value Decomposition for Connectivity Analysis [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 1 download 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 (~þinspace80%) 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.
@Article{rubega2018,
author="Rubega, M.
and Carboni, M.
and Seeber, M.
and Pascucci, D.
and Tourbier, S.
and Toscano, G.
and Van Mierlo, P.
and Hagmann, P.
and Plomp, G.
and Vulliemoz, S.
and Michel, C. M.",
title="Estimating EEG Source Dipole Orientation Based on Singular-value Decomposition for Connectivity Analysis",
journal="Brain Topography",
year="2018",
month="Dec",
day="03",
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 ({\textasciitilde}{\thinspace}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.",
issn="1573-6792",
doi="10.1007/s10548-018-0691-2",
url="https://doi.org/10.1007/s10548-018-0691-2"
}
Downloads: 1
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