Assessment of Multivariate Neural Time Series by Phase Synchrony Clustering in a Time-Frequency-Topography Representation
Joint Authors
Yáñez-Suárez, Oscar
Porta-Garcia, M. A.
Valdés-Cristerna, Raquel
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-03-21
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Porta-Garcia, M. A.& Valdés-Cristerna, Raquel& Yáñez-Suárez, Oscar. 2018. Assessment of Multivariate Neural Time Series by Phase Synchrony Clustering in a Time-Frequency-Topography Representation. Computational Intelligence and Neuroscience،Vol. 2018, no. 2018, pp.1-15.
https://search.emarefa.net/detail/BIM-1130638
Modern Language Association (MLA)
Porta-Garcia, M. A.…[et al.]. Assessment of Multivariate Neural Time Series by Phase Synchrony Clustering in a Time-Frequency-Topography Representation. Computational Intelligence and Neuroscience No. 2018 (2018), pp.1-15.
https://search.emarefa.net/detail/BIM-1130638
American Medical Association (AMA)
Porta-Garcia, M. A.& Valdés-Cristerna, Raquel& Yáñez-Suárez, Oscar. Assessment of Multivariate Neural Time Series by Phase Synchrony Clustering in a Time-Frequency-Topography Representation. Computational Intelligence and Neuroscience. 2018. Vol. 2018, no. 2018, pp.1-15.
https://search.emarefa.net/detail/BIM-1130638
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1130638