Comparison of Causality Network Estimation in the Sensor and Source Space: Simulation and Application on EEG - Koutlis, Christos; Kimiskidis, Vasilios K.; Kugiumtzis, Dimitris (Frontiers in Network Physiology, 2021)
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.