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Related Experiment Video

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Cortical Source Analysis of High-Density EEG Recordings in Children
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Comparison of Causality Network Estimation in the Sensor and Source Space: Simulation and Application on EEG.

Christos Koutlis1, Vasilios K Kimiskidis2, Dimitris Kugiumtzis3

  • 1Information Technologies Institute, Centre of Research and Technology Hellas, Thessaloniki, Greece.

Frontiers in Network Physiology
|March 17, 2023
PubMed
Summary

Connectivity network structures differ between sensor and source space EEG data. Analyzing data in sensor space improves discrimination of network types and epileptiform discharges compared to source space analysis.

Keywords:
Granger causalitybrain networksmulti-channel EEG analysissLORETAsensor space analysissource space analysis

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Biophysics

Background:

  • Estimating system connectivity is crucial, particularly in neuroscience for understanding brain networks.
  • Electroencephalogram (EEG) data analysis often employs Granger causality and source localization techniques like standardized low resolution electromagnetic tomography (sLORETA).
  • These methods aim to improve the reliability of brain network data by transforming it into source space.

Purpose of the Study:

  • To compare connectivity structures estimated in sensor space versus source space using sLORETA transformation.
  • To evaluate the impact of sensor vs. source space analysis on discriminating network types (random, small-world, scale-free).
  • To assess the discriminative ability of network topological indices for identifying epileptiform discharges (ED) in EEG data.

Main Methods:

  • Connectivity was estimated using Granger causality and related concepts.
  • sLORETA transformation was applied to EEG data.
  • Simulated high-dimensional systems and real EEG data with ED were analyzed.
  • Network topological indices were computed and compared between sensor and source spaces.

Main Results:

  • Estimated causality network structures significantly differed between sensor and source spaces for both simulated and real EEG data.
  • Network types (random, small-world, scale-free) were better discriminated using sensor space data.
  • Discriminative ability of network indices for ED was significantly higher in sensor space compared to source space.

Conclusions:

  • Causality networks derived from sensor and source space EEG data exhibit distinct structural properties.
  • Sensor space analysis appears more effective for network type discrimination and identifying ED.
  • Further research is needed to fully understand the relationship between sensor and source space representations in EEG analysis.