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Statistical models for brain signals with properties that evolve across trials.

Hernando Ombao1, Mark Fiecas2, Chee-Ming Ting3

  • 1Statistics Program, King Abdullah University of Science and Technology (KAUST), Saudi Arabia.

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|December 11, 2017
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Summary

This study introduces two models, MS-VAR and SEv-LSP, to analyze dynamic brain connectivity using electroencephalograms (EEGs). These models reveal evolving neural patterns during cognitive experiments, offering insights into learning and habituation.

Keywords:
Autoregressive modelCoherenceMarkov-switching modelPartial directed coherenceSpectral representationState-space

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Signal Processing

Background:

  • Brain responses change during experiments, possibly due to learning or habituation.
  • Analyzing dynamic brain connectivity is crucial for understanding cognitive processes.

Purpose of the Study:

  • To present two novel statistical models for analyzing dynamic brain connectivity from electroencephalograms (EEGs).
  • To model evolving brain responses and connectivity across repeated trials in cognitive experiments.

Main Methods:

  • Developed Markovian regime-switching vector autoregressive (MS-VAR) model for state-switching brain processes.
  • Developed slowly evolutionary locally stationary process (SEv-LSP) model for oscillatory activity and cross-correlations.
  • Derived time-evolving functional and effective connectivity metrics from model parameters.

Main Results:

  • Both models successfully estimated cross-trial connectivity in an auditory oddball experiment.
  • Demonstrated dynamic changes in brain connectivity patterns over experimental trials.
  • Observed significant inter-subject variability in connectivity evolution.

Conclusions:

  • The MS-VAR and SEv-LSP models provide robust frameworks for studying dynamic brain connectivity.
  • These models can characterize neural adaptation, learning, and habituation during cognitive tasks.
  • Findings highlight the importance of considering temporal dynamics in EEG analysis.