Emotion recognition based on time-frequency distribution of EEG signals using multivariate synchrosqueezing transform

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dc.authorid0000-0003-4236-3646en_US
dc.contributor.authorMert, Ahmet
dc.contributor.authorAkan, Aydin
dc.date.accessioned2021-03-20T20:13:02Z
dc.date.available2021-03-20T20:13:02Z
dc.date.issued2018
dc.departmentBTÜ, Mühendislik ve Doğa Bilimleri Fakültesi, Mekatronik Mühendisliği Bölümüen_US
dc.descriptionMert, Ahmt/0000-0003-4236-3646; Akan, Aydin/0000-0001-8894-5794;en_US
dc.description.abstractThis paper investigates the feasibility of using time-frequency (TF) representation of EEG signals for emotional state recognition. A recent and advanced TF analyzing method, multivariate synchrosqueezing transform (MSST) is adopted as a feature extraction method due to multi-channel signal processing and compact component localization capabilities. First, the 32 participants' EEG recordings from DEAP emotional EEG database are analyzed using MSST to reveal oscillations. Second, independent component analysis (ICA), and feature selection are applied to reduce the high dimensional 2D TF distribution without losing distinctive component information in the 2D image. Thus, only one method for feature extraction using MSST is performed to analyze time, and frequency-domain properties of the EEG signals instead of using some signal analyzing combinations (e.g., power spectral density, energy in bands, Hjorth parameters, statistical values, and time differences etc.). As well, the TF-domain reduction performance of ICA is compared to non-negative matrix factorization (NMF) to discuss the accuracy levels of high/low arousal, and high/low valence emotional state recognition. The proposed MSST-ICA feature extraction approach yields up to correct rates of 82.11%, and 82.03% for arousal, and valence state recognition using artificial neural network. The performances of the MSST and ICA are compared with Wigner-Ville distribution (WVD) and NMF to investigate the effects of TF distributions as feature set with reduction techniques on emotion recognition. (C) 2018 Elsevier Inc. All rights reserved.en_US
dc.description.sponsorshipIzmir Katip Celebi University Scientific Research Projects Coordination Unit [2017-ONAP-MUMF-0002]en_US
dc.description.sponsorshipThis study was supported by Izmir Katip Celebi University Scientific Research Projects Coordination Unit: Project number 2017-ONAP-MUMF-0002.en_US
dc.identifier.doi10.1016/j.dsp.2018.07.003en_US
dc.identifier.endpage115en_US
dc.identifier.issn1051-2004
dc.identifier.issn1095-4333
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage106en_US
dc.identifier.urihttp://doi.org/10.1016/j.dsp.2018.07.003
dc.identifier.urihttps://hdl.handle.net/20.500.12885/772
dc.identifier.volume81en_US
dc.identifier.wosWOS:000445307000012en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorMert, Ahmet
dc.language.isoenen_US
dc.publisherAcademic Press Inc Elsevier Scienceen_US
dc.relation.ispartofDigital Signal Processingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectEmotion recognitionen_US
dc.subjectElectroencephalogramen_US
dc.subjectMultivariate synchrosqueezing transformen_US
dc.subjectWigner-Ville distributionen_US
dc.subjectIndependent component analysisen_US
dc.titleEmotion recognition based on time-frequency distribution of EEG signals using multivariate synchrosqueezing transformen_US
dc.typeArticleen_US

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