Markov Model-Based Method to Analyse Time-Varying Networks in EEG Task-Related Data

Nitin J. Williams*, Ian Daly, Slawomir J. Nasuto

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

Abstract

The dynamic nature of functional brain networks is being increasingly recognized in cognitive neuroscience, and methods to analyse such time-varying networks in EEG/MEG data are required. In this work, we propose a pipeline to characterize time-varying networks in single-subject EEG task-related data and further, evaluate its validity on both simulated and experimental datasets. Pre-processing is done to remove channel-wise and trial-wise differences in activity. Functional networks are estimated from short non-overlapping time windows within each "trial," using a sparse-MVAR (Multi-Variate Auto-Regressive) model. Functional "states" are then identified by partitioning the entire space of functional networks into a small number of groups/symbols via k -means clustering.The multi-trial sequence of symbols is then described by a Markov Model (MM). We show validity of this pipeline on realistic electrode-level simulated EEG data, by demonstrating its ability to discriminate "trials" from two experimental conditions in a range of scenarios. We then apply it to experimental data from two individuals using a Brain-Computer Interface (BCI) via a P300 oddball task. Using just the Markov Model parameters, we obtain statistically significant discrimination between target and non-target trials. The functional networks characterizing each 'state' were also highly similar between the two individuals. This work marks the first application of the Markov Model framework to infer time-varying networks from EEG/MEG data. Due to the pre-processing, results from the pipeline are orthogonal to those from conventional ERP averaging or a typical EEG microstate analysis. The results provide powerful proof-of-concept for a Markov model-based approach to analyzing the data, paving the way for its use to track rapid changes in interaction patterns as a task is being performed. MATLAB code for the entire pipeline has been made available.

Original languageEnglish
Article number76
Number of pages18
JournalFRONTIERS IN COMPUTATIONAL NEUROSCIENCE
Volume12
DOIs
Publication statusPublished - 21 Sep 2018
MoE publication typeA1 Journal article-refereed

Keywords

  • EEG/MEG dynamic connectivity
  • EEG/MEG time-varying networks
  • sparse-MVAR modeling
  • markov modeling
  • granger causality
  • effective connectivity
  • FUNCTIONAL CONNECTIVITY
  • NEUROCOGNITIVE NETWORKS
  • RESTING-STATE
  • SPECTRAL-ANALYSIS
  • BRAIN NETWORKS
  • MEG
  • DYNAMICS
  • COGNITION
  • TUTORIAL
  • PRINCIPLES

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