IDEAS home Printed from https://ideas.repec.org/a/igg/jthi00/v15y2019i3p14-27.html
   My bibliography  Save this article

Learning Advanced Brain Computer Interface Technology: Comparing CSP Algorithm and WPA Algorithm for EEG Feature Extraction

Author

Listed:
  • Wang Tao

    (Shandong Jianzhu University, Jinan, China)

  • Wu Linyan

    (Shandong Jianzhu University, Jinan, China)

  • Li Yanping

    (Shandong Jianzhu University, Jinan, China)

  • Gao Nuo

    (Shandong Jianzhu University, Jinan, China)

  • Zhang Weiran

    (Shandong Jianzhu University, Jinan, China)

Abstract

Feature extraction is an important step in electroencephalogram (EEG) processing of motor imagery, and the feature extraction of EEG directly affects the final classification results. Through the analysis of various feature extraction methods, this article finally selects Common Spatial Patterns (CSP) and wavelet packet analysis (WPA) to extract the feature and uses Support Vector Machine (SVM) to classify and compare these extracted features. For the EEG data provided by GRAZ University, the accuracy rate of feature extraction using CSP algorithm is 85.5%, and the accuracy rate of feature extraction using wavelet packet analysis is 92%. Then this paper analyzes the EEG data collected by Emotiv epoc+ system. The classification accuracy of wavelet packet extracted features can still be maintained at more than 80%, while the classification accuracy of CSP extracted feature is decreased obviously. Experimental results show that the method of wavelet packet analysis towards competition data and Emotiv epoc+ system data can both get a desirable outcome.

Suggested Citation

  • Wang Tao & Wu Linyan & Li Yanping & Gao Nuo & Zhang Weiran, 2019. "Learning Advanced Brain Computer Interface Technology: Comparing CSP Algorithm and WPA Algorithm for EEG Feature Extraction," International Journal of Technology and Human Interaction (IJTHI), IGI Global, vol. 15(3), pages 14-27, July.
    • Handle: RePEc:igg:jthi00:v:15:y:2019:i:3:p:14-27
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJTHI.2019070102
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jthi00:v:15:y:2019:i:3:p:14-27. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.