1 Ocak 2012 Dergi makalesi Açık Erişim

Aydemir, Onder; Pourzare, Shahin; Kayikcioglu, Temel

DataCite XML

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  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/86313</identifier>
  <creators>
    <creator>
      <creatorName>Aydemir, Onder</creatorName>
      <givenName>Onder</givenName>
      <familyName>Aydemir</familyName>
      <affiliation>Karadeniz Tech Univ, Dep Elect &amp; Elect Engn, TR-61080 Trabzon, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Pourzare, Shahin</creatorName>
      <givenName>Shahin</givenName>
      <familyName>Pourzare</familyName>
      <affiliation>Karadeniz Tech Univ, Dep Elect &amp; Elect Engn, TR-61080 Trabzon, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Kayikcioglu, Temel</creatorName>
      <givenName>Temel</givenName>
      <familyName>Kayikcioglu</familyName>
      <affiliation>Karadeniz Tech Univ, Dep Elect &amp; Elect Engn, TR-61080 Trabzon, Turkey</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Classifying Various Emg And Eog Artifacts In Eeg Signals</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2012</publicationYear>
  <dates>
    <date dateType="Issued">2012-01-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/86313</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.81043/aperta.86312</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.81043/aperta.86313</relatedIdentifier>
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  <rightsList>
    <rights rightsURI="http://www.opendefinition.org/licenses/cc-by">Creative Commons Attribution</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">EEG is the most popular potential non-invasive interface, mainly due to its fine temporal resolution, ease of use, portability and low set-up cost. However, it has some limitations. The main limitation is that EEG is frequently contaminated by various artifacts. In this paper, a novel approach to classify various electromyography and electrooculography artifacts in EEG signals is presented. EEG signals were acquired at the Department of Electrical and Electronics Engineering Karadeniz Technical University from three healthy human subjects in age groups between 28 and 30 years old and on two different days. Extracted feature vectors based on root mean square, polynomial fitting and Hjorth descriptors were classified by k-nearest neighbor algorithm. The proposed method was successfully applied to the data sets and achieved an average classification rate of 94% on the test data.</description>
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