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Connectivity analysis of multichannel EEG signals using recurrence based phase synchronization technique.

D Rangaprakash1

  • 1Department of Electrical Communication Engineering, Indian Institute of Science, Bangalore 560012, India.

Computers in Biology and Medicine
|February 18, 2014
PubMed
Summary

This study introduces a new nonlinear method, correlation between probabilities of recurrence (CPR), for analyzing brain connectivity using electroencephalographic (EEG) signals. CPR effectively distinguishes brain states like epileptic seizures and identifies epilepsy foci, offering diagnostic potential.

Keywords:
Brain connectivityBrain headmapCorrelation between probabilities of recurrenceEEG signalsEpileptic seizureLinear correlationPhase synchronizationRecurrence quantification analysis

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Area of Science:

  • Neuroscience
  • Nonlinear dynamics
  • Biophysics

Background:

  • Real-world biological systems, like the human brain, exhibit inherent nonlinearity, posing challenges for traditional linear or parametric nonlinear modeling approaches.
  • Previous research predominantly utilized linear models or parametric nonlinear models, limiting the comprehensive understanding of complex brain function and connectivity.

Purpose of the Study:

  • To introduce and validate a novel non-parametric nonlinear measure, correlation between probabilities of recurrence (CPR), for assessing brain connectivity.
  • To demonstrate the utility of CPR in analyzing multichannel electroencephalographic (EEG) signals for brain function studies.
  • To showcase CPR's ability to differentiate between various brain states and its diagnostic potential in neurological conditions.

Main Methods:

  • Proposed a novel nonlinear phase synchronization measure based on recurrences: correlation between probabilities of recurrence (CPR).
  • Applied CPR to multichannel electroencephalographic (EEG) signals to analyze brain connectivity.
  • Utilized thresholded CPR matrices and K-means clustering for comparing CPR with linear correlation in discriminating brain states.

Main Results:

  • Brain connectivity analysis using CPR revealed distinct patterns and numbers of connections between epileptic seizure and pre-seizure states, as well as between eyes-open and eyes-closed conditions.
  • CPR demonstrated a superior ability to discriminate between seizure and pre-seizure states compared to linear correlation, evidenced by larger cluster centroid distances in K-means clustering.
  • Headmap visualization derived from CPR effectively identified the focus of focal epilepsy, indicating significant diagnostic value.

Conclusions:

  • The non-parametric CPR method offers a robust, data-driven approach for investigating nonlinear brain dynamics and connectivity with minimal assumptions.
  • CPR provides meaningful insights into brain synchronization and connectivity, outperforming linear methods in discriminating critical brain states like epileptic seizures.
  • The diagnostic potential of CPR, particularly in localizing epileptic foci, highlights its clinical relevance for neurological assessments.