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

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dc.authorid0000-0003-4236-3646en_US
dc.authorscopusid16835036500en_US
dc.contributor.authorMert, Ahmet
dc.contributor.authorCelik H.H.
dc.date.accessioned2022-04-05T08:18:49Z
dc.date.available2022-04-05T08:18:49Z
dc.date.issued2021en_US
dc.departmentBTÜ, Mühendislik ve Doğa Bilimleri Fakültesi, Mekatronik Mühendisliği Bölümüen_US
dc.description.abstractThe feasibility of using time-frequency (TF) ridges estimation is investigated on multi-channel electroencephalogram (EEG) signals for emotional recognition. Without decreasing accuracy rate of the valence/arousal recognition, the informative component extraction with low computational cost will be examined using multivariate ridge estimation. The advanced TF representation technique called multivariate synchrosqueezing transform (MSST) is used to obtain well-localized components of multi-channel EEG signals. Maximum-energy components in the 2D TF distribution are determined using TF-ridges estimation to extract instantaneous frequency and instantaneous amplitude, respectively. The statistical values of the estimated ridges are used as a feature vector to the inputs of machine learning algorithms. Thus, component information in multi-channel EEG signals can be captured and compressed into low dimensional space for emotion recognition. Mean and variance values of the five maximum-energy ridges in the MSST based TF distribution are adopted as feature vector. Properties of five TF-ridges in frequency and energy plane (e.g., mean frequency, frequency deviation, mean energy, and energy deviation over time) are computed to obtain 20-dimensional feature space. The proposed method is performed on the DEAP emotional EEG recordings for benchmarking, and the recognition rates are yielded up to 71.55, and 70.02% for high/low arousal, and high/low valence, respectively.en_US
dc.identifier.doi10.1515/bmt-2020-0295en_US
dc.identifier.endpage352en_US
dc.identifier.issn00135585
dc.identifier.issue4en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage345en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12885/1878
dc.identifier.volume66en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorMert, Ahmet
dc.language.isoenen_US
dc.publisherDe Gruyter Open Ltden_US
dc.relation.ispartofBiomedizinische Techniken_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectElectroencephalogramen_US
dc.subjectEmotion recognitionen_US
dc.subjectMultivariate synchrosqueezing transformen_US
dc.subjectTime-frequency ridgesen_US
dc.titleEmotion recognition using time-frequency ridges of EEG signals based on multivariate synchrosqueezing transformen_US
dc.typeArticleen_US

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