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Related Experiment Video

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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
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Brain Connectivity Estimation Pitfall in Multiple Trials of Electroencephalography Data.

Vida Mehdizadehfar1, Fanaz Ghassemi1, Ali Fallah1

  • 1Department of Bioelectric, School of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran.

Basic and Clinical Neuroscience
|December 5, 2023
PubMed
Summary

Brain connectivity estimation using electroencephalography (EEG) requires calculating connectivity per trial and averaging results. Small trial numbers yield unreliable, inflated connectivity values, while larger numbers improve model fit and reliability.

Keywords:
Auto-regressive modelConnectivity analysisElectroencephalography (EEG)Granger causality (GC)Multiple trials

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Area of Science:

  • Neuroscience
  • Signal Processing
  • Biomedical Engineering

Background:

  • Electroencephalography (EEG) offers high temporal resolution ideal for brain connectivity analysis.
  • Calculating connectivity from multi-trial EEG data presents challenges regarding trial inclusion and averaging.
  • Existing methods lack clarity on optimal trial handling for accurate connectivity estimation.

Purpose of the Study:

  • To investigate the impact of trial number on brain connectivity estimation using EEG.
  • To determine the most reliable method for computing connectivity from multi-trial EEG data.
  • To provide guidelines for accurate brain connectivity analysis in neuroscience research.

Main Methods:

  • Simulated EEG data with known auto-regressive (AR) coefficients were generated.
  • Multivariate autoregressive (MVAR) models were extracted using varying numbers of trials.
  • Granger causality (GC) and Partial Directed Coherence (PDC) were employed to estimate connectivity.

Main Results:

  • Connectivity estimates were significantly higher and more variable with small trial numbers (5-10 trials).
  • Increasing the number of trials improved MVAR model fit and led to converged connectivity values.
  • Findings from simulated data were corroborated by analyses on real EEG data.

Conclusions:

  • Brain connectivity should be calculated for each trial individually, followed by averaging the results.
  • Larger trial numbers enhance MVAR model appropriateness and the reliability of connectivity estimations.
  • Connectivity estimations based on insufficient trial numbers are considered invalid and misleading.