Markov Model-based method to analyse time-varying networks in EEG task-related data

Williams, N. J., Daly, I. and Nasuto, S. J. ORCID: https://orcid.org/0000-0001-9414-9049 (2018) Markov Model-based method to analyse time-varying networks in EEG task-related data. Frontiers in Computational Neuroscience, 12. 76. ISSN 1662-5188 doi: 10.3389/fncom.2018.00076

Abstract/Summary

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” fromtwo 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.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/79003
Identification Number/DOI	10.3389/fncom.2018.00076
Refereed	Yes
Divisions	Interdisciplinary Research Centres (IDRCs) > Centre for Integrative Neuroscience and Neurodynamics (CINN) Life Sciences > School of Biological Sciences > Department of Bio-Engineering
Publisher	Frontiers
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