• DocumentCode
    629541
  • Title

    Classification of EEG signals by using support vector machines

  • Author

    Bayram, K. Sercan ; Kizrak, M. Ayyuce ; Bolat, B.

  • Author_Institution
    Electr. & Electron. Eng. Dept., Halic Univ., Istanbul, Turkey
  • fYear
    2013
  • fDate
    19-21 June 2013
  • Firstpage
    1
  • Lastpage
    3
  • Abstract
    In 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.
  • Keywords
    electroencephalography; medical signal processing; signal classification; support vector machines; EEG signal classification; feature selection process; kernel function; planning; support vector machines; Accuracy; Band-pass filters; Classification algorithms; Electroencephalography; Kernel; Planning; Support vector machines; EEG; feature selection; suport vector machines;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Innovations in Intelligent Systems and Applications (INISTA), 2013 IEEE International Symposium on
  • Conference_Location
    Albena
  • Print_ISBN
    978-1-4799-0659-8
  • Type

    conf

  • DOI
    10.1109/INISTA.2013.6577636
  • Filename
    6577636
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=629541