Format

Send to

Choose Destination
Brain Topogr. 2019 Jul;32(4):704-719. doi: 10.1007/s10548-018-0691-2. Epub 2018 Dec 3.

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

Author information

1
Functional Brain Mapping Lab, Department of Fundamental Neurosciences, University of Geneva, Campus Biotech, Chemin des Mines 9, 1202, Geneva, Switzerland. maria.rubega@unige.ch.
2
Functional Brain Mapping Lab, Department of Fundamental Neurosciences, University of Geneva, Campus Biotech, Chemin des Mines 9, 1202, Geneva, Switzerland.
3
EEG and Epilepsy Unit, University Hospital of Geneva, Geneva, Switzerland.
4
Perceptual Networks Group, Department of Psychology, University of Fribourg, Fribourg, Switzerland.
5
Department of Radiology, University Hospital of Lausanne, Lausanne, Switzerland.
6
Unit of Sleep Medicine and Epilepsy, C. Mondino National Neurological Institute, Pavia, Italy.
7
Medical Image and Signal Processing Group, Department of Electronics and Information Systems, Ghent University, Ghent, Belgium.
8
Lemanic Biomedical Imaging Centre (CIBM), Lausanne, Geneva, Switzerland.

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 (~‚ÄČ80%) 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.

KEYWORDS:

Dipole orientation; EEG; Epilepsy; Source space activity; Visual evoked potentials

PMID:
30511174
DOI:
10.1007/s10548-018-0691-2

Supplemental Content

Full text links

Icon for Springer
Loading ...
Support Center