IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0074433.html
   My bibliography  Save this article

Z-Score Linear Discriminant Analysis for EEG Based Brain-Computer Interfaces

Author

Listed:
  • Rui Zhang
  • Peng Xu
  • Lanjin Guo
  • Yangsong Zhang
  • Peiyang Li
  • Dezhong Yao

Abstract

Linear discriminant analysis (LDA) is one of the most popular classification algorithms for brain-computer interfaces (BCI). LDA assumes Gaussian distribution of the data, with equal covariance matrices for the concerned classes, however, the assumption is not usually held in actual BCI applications, where the heteroscedastic class distributions are usually observed. This paper proposes an enhanced version of LDA, namely z-score linear discriminant analysis (Z-LDA), which introduces a new decision boundary definition strategy to handle with the heteroscedastic class distributions. Z-LDA defines decision boundary through z-score utilizing both mean and standard deviation information of the projected data, which can adaptively adjust the decision boundary to fit for heteroscedastic distribution situation. Results derived from both simulation dataset and two actual BCI datasets consistently show that Z-LDA achieves significantly higher average classification accuracies than conventional LDA, indicating the superiority of the new proposed decision boundary definition strategy.

Suggested Citation

  • Rui Zhang & Peng Xu & Lanjin Guo & Yangsong Zhang & Peiyang Li & Dezhong Yao, 2013. "Z-Score Linear Discriminant Analysis for EEG Based Brain-Computer Interfaces," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-7, September.
  • Handle: RePEc:plo:pone00:0074433
    DOI: 10.1371/journal.pone.0074433
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0074433
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0074433&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0074433?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ke Yu & Hasan AI-Nashash & Nitish Thakor & Xiaoping Li, 2014. "The Analytic Bilinear Discrimination of Single-Trial EEG Signals in Rapid Image Triage," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-10, June.

    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:plo:pone00:0074433. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.