Emotion recognition from EEG signals by using multivariate empirical mode decomposition

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dc.authorid0000-0003-4236-3646en_US
dc.contributor.authorMert, Ahmet
dc.contributor.authorAkan, Aydin
dc.date.accessioned2021-03-20T20:13:22Z
dc.date.available2021-03-20T20:13:22Z
dc.date.issued2018
dc.departmentBTÜ, Mühendislik ve Doğa Bilimleri Fakültesi, Mekatronik Mühendisliği Bölümüen_US
dc.descriptionMert, Ahmt/0000-0003-4236-3646; Akan, Aydin/0000-0001-8894-5794en_US
dc.description.abstractThis paper explores the advanced properties of empirical mode decomposition (EMD) and its multivariate extension (MEMD) for emotion recognition. Since emotion recognition using EEG is a challenging study due to nonstationary behavior of the signals caused by complicated neuronal activity in the brain, sophisticated signal processing methods are required to extract the hidden patterns in the EEG. In addition, multichannel analysis is another issue to be considered when dealing with EEG signals. EMD is a recently proposed iterative method to analyze nonlinear and nonstationary time series. It decomposes a signal into a set of oscillations called intrinsic mode functions (IMFs) without requiring a set of basis functions. In this study, a MEMD-based feature extraction method is proposed to process multichannel EEG signals for emotion recognition. The multichannel IMFs extracted by MEMD are analyzed using various time and frequency domain techniques such as power ratio, power spectral density, entropy, Hjorth parameters and correlation as features of valance and arousal scales of the participants. The proposed method is applied to the DEAP emotional EEG data set, and the results are compared with similar previous studies for benchmarking.en_US
dc.description.sponsorshipUniversity of IstanbulIstanbul University [45259, 54959]en_US
dc.description.sponsorshipThis work was partially supported by The Research Fund of The University of Istanbul, Project numbers 45259 and 54959.en_US
dc.identifier.doi10.1007/s10044-016-0567-6en_US
dc.identifier.endpage89en_US
dc.identifier.issn1433-7541
dc.identifier.issn1433-755X
dc.identifier.issue1en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage81en_US
dc.identifier.urihttp://doi.org/10.1007/s10044-016-0567-6
dc.identifier.urihttps://hdl.handle.net/20.500.12885/852
dc.identifier.volume21en_US
dc.identifier.wosWOS:000425293800007en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorMert, Ahmet
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofPattern Analysis And Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectEmpirical mode decompositionen_US
dc.subjectMultivariate empirical mode decompositionen_US
dc.subjectEmotion recognitionen_US
dc.subjectElectroencephalogramen_US
dc.titleEmotion recognition from EEG signals by using multivariate empirical mode decompositionen_US
dc.typeArticleen_US

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