Classification of EEG Signals in Neuromarketing Implementation Task

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dc.contributor.authorOran, Samet
dc.contributor.authorGursel, Amira Tandirovic
dc.date.accessioned2025-01-06T17:29:42Z
dc.date.available2025-01-06T17:29:42Z
dc.date.issued2021
dc.description13th International Conference on Electrical and Electronics Engineering, ELECO 2021 -- 25 November 2021 through 27 November 2021 -- Virtual, Bursa -- 176537
dc.description.abstractIn this work, EEG signals that are showing the frequency of the power bands were examined by wavelet power spectrum through neuromarketing outline in order to predict purchaser appetites while they look E-commerce goods. When extraction of these power bands, fixed overlap segment and 3 different sample lengths were counted in sliding window technique. k-NN was implemented for evaluating classification accuracy. The best result, 70.24% k-NN accuracy was obtained for 2-seconds sample length. © 2021 Chamber of Turkish Electrical Engineers.
dc.identifier.doi10.23919/ELECO54474.2021.9677857
dc.identifier.endpage228
dc.identifier.isbn978-605011437-9
dc.identifier.scopus2-s2.0-85125277871
dc.identifier.startpage224
dc.identifier.urihttps://doi.org/10.23919/ELECO54474.2021.9677857
dc.identifier.urihttps://hdl.handle.net/20.500.14669/1289
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof2021 13th International Conference on Electrical and Electronics Engineering, ELECO 2021
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectEEG Classification
dc.subjectk-NN
dc.subjectNeuro-marketing
dc.subjectRandom Forest
dc.subjectSliding Window
dc.subjectWavelet Spectrum
dc.titleClassification of EEG Signals in Neuromarketing Implementation Task
dc.typeConference Object

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