IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/6645322.html
   My bibliography  Save this article

Single-Trial EEG Classification via Common Spatial Patterns with Mixed Lp- and Lq-Norms

Author

Listed:
  • Qian Cai
  • Weiqiang Gong
  • Yue Deng
  • Haixian Wang

Abstract

As a multichannel spatial filtering technique, common spatial patterns (CSP) have been successfully applied in brain-computer interfaces (BCI) community based on electroencephalogram (EEG). However, it is sensitive to outliers because of the employment of the L2-norm in its formulation. It is beneficial to perform robust modelling for CSP. In this paper, we propose a robust framework, called CSP-Lp/q, by formulating the variances of two EEG classes with Lp- and Lq-norms ( ) separately. The method CSP-Lp/q with mixed Lp- and Lq-norms takes the class-wise difference into account in formulating the sample dispersion. We develop an iterative algorithm to optimize the objective function of CSP-Lp/q and show its monotonity theoretically. The superiority of the proposed CSP-Lp/q technique is experimentally demonstrated on three real EEG datasets of BCI competitions.

Suggested Citation

  • Qian Cai & Weiqiang Gong & Yue Deng & Haixian Wang, 2021. "Single-Trial EEG Classification via Common Spatial Patterns with Mixed Lp- and Lq-Norms," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-13, June.
    • Handle: RePEc:hin:jnlmpe:6645322
    DOI: 10.1155/2021/6645322
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2021/6645322.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/MPE/2021/6645322.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2021/6645322?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
    ---><---

    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:hin:jnlmpe:6645322. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.