AUTHOR=Koutlis Christos , Kimiskidis Vasilios K. , Kugiumtzis Dimitris 

TITLE=Comparison of Causality Network Estimation in the Sensor and Source Space: Simulation and Application on EEG

JOURNAL=Frontiers in Network Physiology

VOLUME=Volume 1 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/network-physiology/articles/10.3389/fnetp.2021.706487

DOI=10.3389/fnetp.2021.706487

ISSN=2674-0109

ABSTRACT=The usage of methods for the estimation of the true underlying
connectivity among the observed variables of a system is
increasing, especially in the domain of neuroscience. Granger
causality and similar concepts are employed for the estimation of
the brain network from electroencephalogram (EEG) data. Also
source localization techniques, such as the standardized low
resolution electromagnetic tomography (sLORETA), are widely used
for more reliable data and results. In this work, connectivity
structures are estimated in the sensor and in the source space
making use of the sLORETA transformation for simulated and for EEG
data with episodes of spontaneous epileptiform discharges (ED).
The comparative simulation study on high-dimensional coupled stochastic
and deterministic systems concludes that the structure of the estimated
causality networks differs in the sensor space and in the source
space. Moreover, different network types, such as random, small-world and scale-free,
can be better discriminated in the sensor space than in the source space.
The simulation results are in agreement with the findings for the EEG epochs 
containing EDs, concluding that the structural change 
(before and during ED) can be better detected in the sensor space.