Emotional Networked maps from EEG signals

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dc.contributor.authorGomez A.
dc.contributor.authorQuintero O.L.
dc.contributor.authorLopez-Celani N.
dc.contributor.authorVilla L.F.
dc.contributor.departmentUniversidad EAFIT. Departamento de Cienciasspa
dc.contributor.researchgroupModelado Matemáticospa
dc.creatorGomez A.
dc.creatorQuintero O.L.
dc.creatorLopez-Celani N.
dc.creatorVilla L.F.
dc.date.accessioned2021-04-12T14:11:50Z
dc.date.available2021-04-12T14:11:50Z
dc.date.issued2020-01-01
dc.description.abstractThe EEG has showed that contains relevant information about recognition of emotional states. It is important to analyze the EEG signals to understand the emotional states not only from a time series approach but also determining the importance of the generating process of these signals, the location of electrodes and the relationship between the EEG signals. From the EEG signals of each emotional state, a functional connectivity measurement was used to construct adjacency matrices: lagged phase synchronization (LPS), averaging adjacency matrices we built a prototype network for each emotion. Based on these networks, we extracted a set node features seeking to understand their behavior and the relationship between them. We found through the strength and degree, the group of representative electrodes for each emotional state, finding differences from intensity of measurement and the spatial location of these electrodes. In addition, analyzing the cluster coefficient, degree, and strength, we find differences between the networks from the spatial patterns associated with the electrodes with the highest coefficient. This analysis can also gain evidence from the connectivity elements shared between emotional states, allowing to cluster emotions and concluding about the relationship of emotions from EEG perspective. © 2020 IEEE.eng
dc.identifierhttps://eafit.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=12191
dc.identifier.doi10.1109/EMBC44109.2020.9175935
dc.identifier.issn05891019
dc.identifier.issn1557170X
dc.identifier.otherWOS;000621592200009
dc.identifier.otherPUBMED;33017924
dc.identifier.otherSCOPUS;2-s2.0-85091012600
dc.identifier.urihttp://hdl.handle.net/10784/27911
dc.language.isoengeng
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85091012600&doi=10.1109%2fEMBC44109.2020.9175935&partnerID=40&md5=ae27cc7bc66d990e79ba8002aea434b9
dc.rightsInstitute of Electrical and Electronics Engineers Inc.
dc.sourceIEEE Engineering in Medicine and Biology Society Conference Proceedings
dc.subject.keywordGraph structureseng
dc.subject.keywordAdjacency matriceseng
dc.subject.keywordCluster coefficientseng
dc.subject.keywordEEG signalseng
dc.subject.keywordEmotional stateeng
dc.subject.keywordFunctional connectivityeng
dc.subject.keywordLagged phaseeng
dc.subject.keywordSpatial locationeng
dc.subject.keywordSpatial patternseng
dc.subject.keywordElectrodeseng
dc.titleEmotional Networked maps from EEG signalseng
dc.typeinfo:eu-repo/semantics/conferencePapereng
dc.typeconferencePapereng
dc.typeinfo:eu-repo/semantics/publishedVersioneng
dc.typepublishedVersioneng
dc.type.localDocumento de conferenciaspa

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