Two-stepped majority voting for efficient EEG-based emotion classification

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dc.authorid0000-0002-2917-3736en_US
dc.contributor.authorIsmael, Aras Masood
dc.contributor.authorAlçin, Ömer Faruk
dc.contributor.authorAbdalla, Karmand Hussein
dc.contributor.authorŞengür, Abdulkadir
dc.date.accessioned2021-06-08T11:55:39Z
dc.date.available2021-06-08T11:55:39Z
dc.date.issued2020en_US
dc.departmentMTÖ Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractIn this paper, a novel approach that is based on two-stepped majority voting is proposed for efficient EEG-based emotion classification. Emotion recognition is important for human-machine interactions. Facial features- and body gestures-based approaches have been generally proposed for emotion recognition. Recently, EEG-based approaches become more popular in emotion recognition. In the proposed approach, the raw EEG signals are initially low-pass filtered for noise removal and band-pass filters are used for rhythms extraction. For each rhythm, the best performed EEG channels are determined based on wavelet-based entropy features and fractal dimension-based features. The k-nearest neighbor (KNN) classifier is used in classification. The best five EEG channels are used in majority voting for getting the final predictions for each EEG rhythm. In the second majority voting step, the predictions from all rhythms are used to get a final prediction. The DEAP dataset is used in experiments and classification accuracy, sensitivity and specificity are used for performance evaluation metrics. The experiments are carried out to classify the emotions into two binary classes such as high valence (HV) vs low valence (LV) and high arousal (HA) vs low arousal (LA). The experiments show that 86.3% HV vs LV discrimination accuracy and 85.0% HA vs LA discrimination accuracy is obtained. The obtained results are also compared with some of the existing methods. The comparisons show that the proposed method has potential in the use of EEG-based emotion classification.en_US
dc.identifier.citationIsmael, A. M., Alçin, Ö. F., Abdalla, K. H., & Şengür, A. (2020). Two-stepped majority voting for efficient EEG-based emotion classification. Brain Informatics, 7(1), 1-12.en_US
dc.identifier.doi10.1186/s40708-020-00111-3
dc.identifier.endpage12en_US
dc.identifier.issn2198-4018en_US
dc.identifier.issn2198-4026en_US
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85091189671en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://doi.org/10.1186/s40708-020-00111-3
dc.identifier.urihttps://hdl.handle.net/20.500.12899/197
dc.identifier.volume7en_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorAlçin, Ömer Faruk
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofBrain Informaticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectEEG-based emotion recognitionen_US
dc.subjectEEG rhythmsen_US
dc.subjectWavelet packet entropiesen_US
dc.subjectFractal dimensionsen_US
dc.subjectMajority votingen_US
dc.titleTwo-stepped majority voting for efficient EEG-based emotion classificationen_US
dc.typeArticleen_US

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