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Classification of EEG Signals by using Support Vector Machines

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dc.contributor.authorBayram, K. Sercan
dc.contributor.authorKizrak, M. Ayyuce
dc.contributor.authorBolat, Bulent
dc.date.accessioned2026-06-27T13:29:39Z
dc.date.issued2013
dc.description.abstractIn this work, EEG signals were classified by support vector machines to detect whether a subject's planning to perform a task or not. Various different kernels were utilized to find the best kernel function and after that, a feature selection process was realized. The results are comparable to the recent works.en
dc.identifier.isbn978-1-4799-0661-1; 978-1-4799-0659-8
dc.identifier.urihttps://hdl.handle.net/20.500.14981/53183
dc.identifier.wos000332186500022
dc.language.isoeng
dc.publisherIEEE
dc.relation.conferenceIEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA)
dc.relation.ispartof2013 IEEE INTERNATIONAL SYMPOSIUM ON INNOVATIONS IN INTELLIGENT SYSTEMS AND APPLICATIONS (IEEE INISTA)
dc.subjectEEG
dc.subjectsuport vector machines
dc.subjectfeature selection
dc.subjectComputer Science
dc.subjectEngineering
dc.titleClassification of EEG Signals by using Support Vector Machines
dc.typeProceedings Paper
dspace.entity.typePublication
local.import.sourceWOS

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