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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
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Complex Pearson Correlation Coefficient for EEG Connectivity Analysis.

Zoran Šverko1,2, Miroslav Vrankić1, Saša Vlahinić1

  • 1Faculty of Engineering, Department of Automation and Electronics, University of Rijeka, 51000 Rijeka, Croatia.

Sensors (Basel, Switzerland)
|February 26, 2022
PubMed
Summary
This summary is machine-generated.

We introduce the complex Pearson correlation coefficient (CPCC) for analyzing electroencephalography (EEG) brain signals. CPCC offers a comprehensive measure of neural connectivity, integrating information from other methods like PLV and wPLI.

Keywords:
EEGcomplex Pearson correlation coefficientsfunctional connectivityphase locking valueweighted phase lag index

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Area of Science:

  • Neuroscience
  • Signal Processing
  • Computational Biology

Background:

  • Neural connections underpin human cognition and behavior.
  • Electroencephalography (EEG) is a key tool for studying brain activity.
  • Existing EEG connectivity measures have limitations.

Purpose of the Study:

  • To propose and evaluate the complex Pearson correlation coefficient (CPCC) for EEG data analysis.
  • To compare CPCC with established connectivity metrics: Phase Locking Value (PLV) and Weighted Phase Lag Index (wPLI).
  • To assess CPCC's ability to account for volume conduction effects in neural connectivity.

Main Methods:

  • Analytical derivation of relationships between CPCC, PLV, and wPLI.
  • Comparative analysis using synthetic and real EEG datasets.
  • Evaluation of connectivity measures across different frequency bands.

Main Results:

  • High correlations observed between CPCC and other measures (r > 0.97 for |CPCC| vs PLV, r > 0.92 for Im(CPCC) vs wPLI).
  • CPCC effectively captures connectivity information, incorporating aspects of both PLV and wPLI.
  • Demonstrated CPCC's utility with and without considering volume conduction.

Conclusions:

  • CPCC is a robust and informative measure for EEG connectivity analysis.
  • The complex-valued nature of CPCC provides a nuanced understanding of neural interactions.
  • CPCC offers a unified index for brain connectivity, simplifying complex signal analysis.