Enhanced deep capsule network for EEG-based emotion recognition

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dc.authoridOzcan, Caner/0000-0002-2854-4005
dc.contributor.authorCizmeci, Huseyin
dc.contributor.authorOzcan, Caner
dc.date.accessioned2024-09-29T15:54:33Z
dc.date.available2024-09-29T15:54:33Z
dc.date.issued2023
dc.departmentKarabük Üniversitesien_US
dc.description.abstractRecently, it has become very popular to use electroencephalogram (EEG) signals in emotion recognition studies. But, EEG signals are much more complex than image and audio signals. There may be inconsistencies even in signals recorded from the same person. Therefore, EEG signals obtained from the human brain must be analyzed and processed accurately and consistently. In addition, traditional algorithms used to classify emotion ignore the neighborhood relationship and hierarchical order within the EEG signals. In this paper, a method including selection of suitable channels from EEG data, feature extraction by Welch power spectral density estimation of selected channels and enhanced capsule network-based classification model is presented. The most important innovation of the method is to adjust the architecture of the capsule network to adapt to the EEG signals. Thanks to the proposed method, 99.51% training and 98.21% test accuracy on positive, negative and neutral emotions were achieved in the Seed EEG dataset. The obtained results were also compared and evaluated with other state-of-the-art methods. Finally, the method was tested with Dreamer and Deap EEG datasets.en_US
dc.description.sponsorshipScientific Research Projects Unit of Karabuk University [FDK-2020-2309]en_US
dc.description.sponsorshipThis work was supported by Scientific Research Projects Unit of Karabuk University under project number FDK-2020-2309. The authors appreciate the financial and scientific support.en_US
dc.identifier.doi10.1007/s11760-022-02251-x
dc.identifier.endpage469en_US
dc.identifier.issn1863-1703
dc.identifier.issn1863-1711
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85130800844en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage463en_US
dc.identifier.urihttps://doi.org/10.1007/s11760-022-02251-x
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4136
dc.identifier.volume17en_US
dc.identifier.wosWOS:000805530600001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer London Ltden_US
dc.relation.ispartofSignal Image and Video Processingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectEmotion recognitionen_US
dc.subjectEEGen_US
dc.subjectFeature extractionen_US
dc.subjectDeep learningen_US
dc.subjectCapsule networken_US
dc.titleEnhanced deep capsule network for EEG-based emotion recognitionen_US
dc.typeArticleen_US

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