Assessment of Multivariate Neural Time Series by Phase Synchrony Clustering in a Time-Frequency-Topography Representation

Most EEG phase synchrony measures are of bivariate nature. Those that are multivariate focus on producing global indices of the synchronization state of the system. Thus, better descriptions of spatial and temporal local interactions are still in demand. A framework for characterization of phase syn...

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Bibliographic Details
Published in:Computational intelligence and neuroscience Vol. 2018; no. 2018; pp. 1 - 15
Main Authors: Porta-Garcia, M. A., Yáñez-Suárez, Oscar, Valdés-Cristerna, Raquel
Format: Journal Article
Language:English
Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2018
Hindawi
John Wiley & Sons, Inc
Subjects:
Algorithms
Analysis
Bivariate analysis
Cluster analysis
Clustering
Clustering (Computers)
Clusters
Cognitive ability
Decomposition
EEG
Eigenvalues
Electrodes
Electroencephalography
Event-related potentials
Fourier transforms
Fuzzy algorithms
Fuzzy logic
Fuzzy systems
Methods
Neurosciences
Phase transitions
Representations
Synchronism
Synchronization
Time series
Topography
ISSN:1687-5265, 1687-5273, 1687-5273
Online Access:Get full text
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Summary:Most EEG phase synchrony measures are of bivariate nature. Those that are multivariate focus on producing global indices of the synchronization state of the system. Thus, better descriptions of spatial and temporal local interactions are still in demand. A framework for characterization of phase synchrony relationships between multivariate neural time series is presented, applied either in a single epoch or over an intertrial assessment, relying on a proposed clustering algorithm, termed Multivariate Time Series Clustering by Phase Synchrony, which generates fuzzy clusters for each multivalued time sample and thereupon obtains hard clusters according to a circular variance threshold; such cluster modes are then depicted in Time-Frequency-Topography representations of synchrony state beyond mere global indices. EEG signals from P300 Speller sessions of four subjects were analyzed, obtaining useful insights of synchrony patterns related to the ERP and even revealing steady-state artifacts at 7.6 Hz. Further, contrast maps of Levenshtein Distance highlight synchrony differences between ERP and no-ERP epochs, mainly at delta and theta bands. The framework, which is not limited to one synchrony measure, allows observing dynamics of phase changes and interactions among channels and can be applied to analyze other cognitive states rather than ERP versus no ERP.
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Academic Editor: Victor H. C. de Albuquerque
ISSN:1687-5265
1687-5273
1687-5273
DOI:10.1155/2018/2406909