Channel selection and feature extraction on deep EEG classification using metaheuristic and Welch PSD

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dc.contributor.authorCizmeci, Huseyin
dc.contributor.authorOzcan, Caner
dc.contributor.authorDurgut, Rafet
dc.date.accessioned2024-09-29T15:51:04Z
dc.date.available2024-09-29T15:51:04Z
dc.date.issued2022
dc.departmentKarabük Üniversitesien_US
dc.description.abstractBrain computer interfaces are important for different application domain such as medical, natural interfaces and entertainment. Besides the difficulty of gathering data from the human brain via different channel probs, preprocessing of data is another different and important task that must be solved in order to get better achievement. Selection of the most active channels is an important problem to achieve high classification accuracy. Metaheuristics are good solutions for selecting the optimal subset from the original set, as they have the ability to obtain an acceptable solution in a reasonable time. At the same time, it is necessary to use the correct feature extraction method so that the data can be properly represented. In addition, traditional deep learning methods used for emotion recognition ignore the spatial properties of EEG signals. This reduces the classification accuracy. In this study, we used artificial bee colony optimization algorithm on the seed dataset to increase the classification accuracy. We implemented and tested four different variations of this algorithm. Then, we extracted the features of the obtained channels with the Welch PSD method. We used enhanced capsule network as a machine learning algorithm and showed the best configuration to solve the problem. At the end of the process, 99.98% training and 99.83% test accuracy rates were obtained.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/s00500-022-07413-0
dc.identifier.endpage10125en_US
dc.identifier.issn1432-7643
dc.identifier.issn1433-7479
dc.identifier.issue19en_US
dc.identifier.scopus2-s2.0-85135976605en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage10115en_US
dc.identifier.urihttps://doi.org/10.1007/s00500-022-07413-0
dc.identifier.urihttps://hdl.handle.net/20.500.14619/3878
dc.identifier.volume26en_US
dc.identifier.wosWOS:000840278400002en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofSoft Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectEEG classificationen_US
dc.subjectArtificial bee colonyen_US
dc.subjectDeep learningen_US
dc.subjectCapsule networksen_US
dc.titleChannel selection and feature extraction on deep EEG classification using metaheuristic and Welch PSDen_US
dc.typeArticleen_US

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