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

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dc.contributor.author Williams, Nitin
dc.contributor.author Daly, Ian
dc.contributor.author Nasuto, Slawomir
dc.date.accessioned 2019-02-06T09:32:02Z
dc.date.available 2019-02-06T09:32:02Z
dc.date.issued 2018
dc.identifier.citation Williams , N , Daly , I & Nasuto , S 2018 , ' Markov Model-Based Method to Analyse Time-Varying Networks in EEG Task-Related Data ' , Frontiers in computational neuroscience , vol. 12 , 76 . https://doi.org/10.3389/fncom.2018.00076
dc.identifier.other PURE: 121945786
dc.identifier.other PURE UUID: 50207927-bc1a-41c1-8fef-920727443ede
dc.identifier.other Scopus: 85054786191
dc.identifier.other WOS: 000445223900001
dc.identifier.uri http://hdl.handle.net/10138/298721
dc.description.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. en
dc.format.extent 18
dc.language.iso eng
dc.relation.ispartof Frontiers in computational neuroscience
dc.rights cc_by
dc.rights.uri info:eu-repo/semantics/openAccess
dc.subject 112 Statistics and probability
dc.subject 113 Computer and information sciences
dc.subject 1184 Genetics, developmental biology, physiology
dc.title Markov Model-Based Method to Analyse Time-Varying Networks in EEG Task-Related Data en
dc.type Article
dc.contributor.organization Department of Languages
dc.contributor.organization Neuroscience Center
dc.description.reviewstatus Peer reviewed
dc.relation.doi https://doi.org/10.3389/fncom.2018.00076
dc.relation.issn 1662-5188
dc.rights.accesslevel openAccess
dc.type.version publishedVersion

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