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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| Veröffentlicht in: | Computational intelligence and neuroscience 2018-01, Vol.2018 (2018), p.1-15 |
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| description | 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. |
| doi_str_mv | 10.1155/2018/2406909 |
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A. ; Yáñez-Suárez, Oscar ; Valdés-Cristerna, Raquel</creator><contributor>de Albuquerque, Victor H. C. ; Victor H C de Albuquerque</contributor><creatorcontrib>Porta-Garcia, M. A. ; Yáñez-Suárez, Oscar ; Valdés-Cristerna, Raquel ; de Albuquerque, Victor H. C. ; Victor H C de Albuquerque</creatorcontrib><description>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.</description><identifier>ISSN: 1687-5265</identifier><identifier>EISSN: 1687-5273</identifier><identifier>DOI: 10.1155/2018/2406909</identifier><identifier>PMID: 29755510</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>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</subject><ispartof>Computational intelligence and neuroscience, 2018-01, Vol.2018 (2018), p.1-15</ispartof><rights>Copyright © 2018 M. A. Porta-Garcia et al.</rights><rights>COPYRIGHT 2018 John Wiley & Sons, Inc.</rights><rights>Copyright © 2018 M. A. Porta-Garcia et al.; This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</rights><rights>Copyright © 2018 M. A. Porta-Garcia et al. 2018</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c456t-72d0ae8b2c54f3ea46ae7b98c13152b3ee23fd3b61bc90748f394b41b6cb02543</cites><orcidid>0000-0002-0173-1759 ; 0000-0002-4249-8877 ; 0000-0002-7524-5833</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5884284/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5884284/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,724,777,781,882,27905,27906,53772,53774</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29755510$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>de Albuquerque, Victor H. C.</contributor><contributor>Victor H C de Albuquerque</contributor><creatorcontrib>Porta-Garcia, M. A.</creatorcontrib><creatorcontrib>Yáñez-Suárez, Oscar</creatorcontrib><creatorcontrib>Valdés-Cristerna, Raquel</creatorcontrib><title>Assessment of Multivariate Neural Time Series by Phase Synchrony Clustering in a Time-Frequency-Topography Representation</title><title>Computational intelligence and neuroscience</title><addtitle>Comput Intell Neurosci</addtitle><description>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. 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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.</description><subject>Algorithms</subject><subject>Analysis</subject><subject>Bivariate analysis</subject><subject>Cluster analysis</subject><subject>Clustering</subject><subject>Clustering (Computers)</subject><subject>Clusters</subject><subject>Cognitive ability</subject><subject>Decomposition</subject><subject>EEG</subject><subject>Eigenvalues</subject><subject>Electrodes</subject><subject>Electroencephalography</subject><subject>Event-related potentials</subject><subject>Fourier transforms</subject><subject>Fuzzy algorithms</subject><subject>Fuzzy logic</subject><subject>Fuzzy systems</subject><subject>Methods</subject><subject>Neurosciences</subject><subject>Phase transitions</subject><subject>Representations</subject><subject>Synchronism</subject><subject>Synchronization</subject><subject>Time series</subject><subject>Topography</subject><issn>1687-5265</issn><issn>1687-5273</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqNkUtv1DAUhSMEoqWwY40ssUGioX7H2SCNRpQilYdgWFuO52biKrGndlKUf4-HGabAitX11f10jo9OUTwn-A0hQlxQTNQF5VjWuH5QnBKpqlLQij08vqU4KZ6kdIOxqASmj4sTWldCCIJPi3mREqQ0gB9RaNHHqR_dnYnOjIA-wRRNj1ZuAPQNooOEmhl96UzK--xtF4Of0bKf0pivfoOcR-YXXl5GuJ3A27lchW3YRLPtZvQVthFSdjKjC_5p8ag1fYJnh3lWfL98t1peldef339YLq5Ly4Ucy4qusQHVUCt4y8BwaaBqamUJI4I2DICyds0aSRpb44qrltW84aSRtsFUcHZWvN3rbqdmgLXN_jmV3kY3mDjrYJz---JdpzfhTgulOFU7gVcHgRhyqDTqwSULfW88hClpipmqMBGSZvTlP-hNmKLP8TJFMZe4ZvU9tTE9aOfbkH3tTlQvhBKcKyZ31PmesjGkFKE9fplgvWte75rXh-Yz_uLPmEf4d9UZeL0HOufX5of7TznIDLTmniYMS6bYT9BUwNI</recordid><startdate>20180101</startdate><enddate>20180101</enddate><creator>Porta-Garcia, M. 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A.</au><au>Yáñez-Suárez, Oscar</au><au>Valdés-Cristerna, Raquel</au><au>de Albuquerque, Victor H. C.</au><au>Victor H C de Albuquerque</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Assessment of Multivariate Neural Time Series by Phase Synchrony Clustering in a Time-Frequency-Topography Representation</atitle><jtitle>Computational intelligence and neuroscience</jtitle><addtitle>Comput Intell Neurosci</addtitle><date>2018-01-01</date><risdate>2018</risdate><volume>2018</volume><issue>2018</issue><spage>1</spage><epage>15</epage><pages>1-15</pages><issn>1687-5265</issn><eissn>1687-5273</eissn><abstract>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. 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| 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 |
| title | Assessment of Multivariate Neural Time Series by Phase Synchrony Clustering in a Time-Frequency-Topography Representation |
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