A study on the effect of psychophysiological signal features on classification methods

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dc.contributor.authorErkan, Erdem
dc.contributor.authorKurnaz, Ismail
dc.date.accessioned2024-09-29T15:57:55Z
dc.date.available2024-09-29T15:57:55Z
dc.date.issued2017
dc.departmentKarabük Üniversitesien_US
dc.description.abstractThe most important factor affecting the performance of a BCI (Brain Cothputer Interface) systems, is classification feature set. Choosing the right features to increase the success of classification is the key point. In BCI systems, signals from brain are used to store into dataset. In this study, BCI Competition III dataset 1 consisting of ECoG (Electrocorticography) signals is preferred. In the first part, in order to decrease the processing load, the number of channels are reduced by eliminating Channels (electrodes) which have low separation success. We developed new algorithm ADA (Arc Detection Algorithm) based on visual channel selection to determine quickly optimal channel subset. Than Obtained Wavelet coefficients by Discrete Wavelet Transform (DWT) and determined classification features from Wavelet coefficients. These features are used to classify by KNN (K Nearest Neighbors), SVM (Support Vector Machine) and LDA (Linear Discriminant Analysis) by different feature set combination. The classification successes of feature combinations which are used in classification are compared. The impact on the classification performance of the right channel and the right property choice is observed. Test results are made with different frequency bands are compared with the same feature set. As a result, the highest classification accuracy of 95% was obtained by selected channels and feature. (C) 2017 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.measurement.2017.01.019
dc.identifier.endpage52en_US
dc.identifier.issn0263-2241
dc.identifier.issn1873-412X
dc.identifier.scopus2-s2.0-85010066677en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage45en_US
dc.identifier.urihttps://doi.org/10.1016/j.measurement.2017.01.019
dc.identifier.urihttps://hdl.handle.net/20.500.14619/5100
dc.identifier.volume101en_US
dc.identifier.wosWOS:000395216100007en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofMeasurementen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectECoGen_US
dc.subjectDiscrete Wavelet Transform (DWT)en_US
dc.subjectClassificationen_US
dc.titleA study on the effect of psychophysiological signal features on classification methodsen_US
dc.typeArticleen_US

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