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Estimating EEG Source Dipole Orientation Based on Singular-value Decomposition for Connectivity Analysis by Rubega, M.; Carboni, M.; Seeber, M.; Pascucci, D.; Tourbier, S.; Toscano, G.; Van Mierlo, P.; Hagmann, P.; Plomp, G.; Vulliemoz, S.; Michel, C. M. is a Neuroscience article available to read on EtoBox.

What is Estimating EEG Source Dipole Orientation Based on Singular-value Decomposition for Connectivity Analysis about?

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 re

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

It is typically read by researchers, students, and practitioners in Neuroscience.

Author
Rubega, M.; Carboni, M.; Seeber, M.; Pascucci, D.; Tourbier, S.; Toscano, G.; Van Mierlo, P.; Hagmann, P.; Plomp, G.; Vulliemoz, S.; Michel, C. M.
Publisher
Springer; Springer-Verlag; Kluwer Academic/Plenum Publishers; Springer Science and Business Media LLC; Research Square (ISSN 0896-0267)
Published
2018
Language
EN
Field
Neuroscience (Life Sciences)

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