Channel Selection from EEG Signals and Application of Support Vector Machine on EEG Data

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dc.contributor.authorArslan, Mustafa Turan
dc.contributor.authorEraldemir, Server Goksel
dc.contributor.authorYildirim, Esen
dc.date.accessioned2024-09-18T20:16:40Z
dc.date.available2024-09-18T20:16:40Z
dc.date.issued2017
dc.departmentHatay Mustafa Kemal Üniversitesien_US
dc.description2017 International Artificial Intelligence and Data Processing Symposium (IDAP) -- SEP 16-17, 2017 -- Malatya, TURKEYen_US
dc.description.abstractIn this study, EEG data recorded during mental arithmetic operations and silent reading were analyzed by discrete wavelet transform and feature vectors were obtained. The obtained feature vectors are classified by Support Vector Machines (SVM). Results are given for 26 channels, all recorded channels, and for 10 most effective channels. Correlation based feature selection based algorithm is used for choosing the most effective channels. Decreasing the number of channels without compromising the accuracy, is an important issue for real time applications for which a short analysis time is crucial. In this study, mental arithmetic and silent reading tasks are classified with an accuracy of 90.71%, a precision rate of 91.03% and F-measure rate of 90.63% on the average using 26 channels, whereas the accuracy, precision and F-measure were 90.44%, 90.61% and 90.08, respectively which were comparable to that of obtained using all channels, for reduced number of channels.en_US
dc.description.sponsorshipIEEE Turkey Sect,Anatolian Scien_US
dc.identifier.isbn978-1-5386-1880-6
dc.identifier.scopus2-s2.0-85039914389en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/20.500.12483/9681
dc.identifier.wosWOS:000426868700066en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeeeen_US
dc.relation.ispartof2017 International Artificial Intelligence and Data Processing Symposium (Idap)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectEEGen_US
dc.subjectdiscrete wavelet transform (DWT)en_US
dc.subjectclassificationen_US
dc.subjectsupport vector machine (SVM)en_US
dc.titleChannel Selection from EEG Signals and Application of Support Vector Machine on EEG Dataen_US
dc.typeConference Objecten_US

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