EEG channel selection using differential evolution algorithm and particle swarm optimization for classification of odorant-stimulated records

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dc.authorid0000-0001-9245-6790en_US
dc.authorid0000-0002-9368-8902en_US
dc.contributor.authorŞeker, Mesut
dc.contributor.authorÖzerdem, Mehmet Siraç
dc.date.accessioned2023-03-15T11:22:21Z
dc.date.available2023-03-15T11:22:21Z
dc.date.issued2021en_US
dc.departmentDicle Üniversitesi, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractA significant advancement has been made in the evolutionary computing and swarm intelligence methods in past decades. These methods have been commonly used to calculate well optimized solutions. Methods select the best elements or cases among set of alternatives. In EEG signal processing applications, efficient channel selection algorithms are required to reduce high dimensionality and remove redundant features. To do this, we examined optimal 5 electrodes out of 14 using Particle Swarm Optimization (PSO) and Differential Evolution Algorithm (DEA). The proposed work is related with pleasant- unpleasant EEG odors classification problem. Classification error rates were calculated by Linear Discriminant Analysis (LDA), k-NN (k Nearest Neighbour), Naive Bayes (NB), Regression Tree (RegTree) classifiers and used as fitness function for optimization algorithms. The results showed that PSO with selected 5 channels gave lowest error rates compared with DEA for all runs. RegTree classifier generated optimal fitness function value among other classifiers. PSO algorithm can effectively support channel selection problem to identify the best channels to maximize classification performance.en_US
dc.identifier.citationŞeker, M. ve Özerdem, M. S. (2021). EEG channel selection using differential evolution algorithm and particle swarm optimization for classification of odorant-stimulated records. European Journal of Technique, 11(2), 120-125.en_US
dc.identifier.doi10.36222/ejt.873351
dc.identifier.endpage125en_US
dc.identifier.issn2536-5134
dc.identifier.issue2en_US
dc.identifier.startpage120en_US
dc.identifier.trdizinid1123552
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1123552
dc.identifier.urihttps://hdl.handle.net/11468/11397
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/1123552
dc.identifier.volume11en_US
dc.indekslendigikaynakTR-Dizin
dc.institutionauthorŞeker, Mesut
dc.institutionauthorÖzerdem, Mehmet Siraç
dc.language.isoenen_US
dc.publisherINESEG Yayıncılıken_US
dc.relation.ispartofEuropean Journal of Technique
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDEAen_US
dc.subjectEEGen_US
dc.subjectChannel selectionen_US
dc.subjectEvolutionary computingen_US
dc.subjectPSOen_US
dc.subjectSwarm intelligenceen_US
dc.titleEEG channel selection using differential evolution algorithm and particle swarm optimization for classification of odorant-stimulated recordsen_US
dc.titleEEG channel selection using differential evolution algorithm and particle swarm optimization for classification of odorant-stimulated records
dc.typeArticleen_US

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