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