IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v34y2019i2d10.1007_s00180-018-0819-6.html
   My bibliography  Save this article

Benefits of functional PCA in the analysis of single-trial auditory evoked potentials

Author

Listed:
  • Jan Koláček

    (Masaryk University)

  • Ondřej Pokora

    (Masaryk University)

  • Daniela Kuruczová

    (Masaryk University)

  • Tzai-Wen Chiu

    (Masaryk University)

Abstract

Evoked potentials reflect neural processing and are widely used to studying sensory perception. Here we applied a functional approach to studying single-trial auditory evoked potentials in the rat model of tinnitus, in which overdoses of salicylate are known to alter sound perception characteristically. Single-trial evoked potential integrals were generated with sound stimuli (tones and clicks) presented systematically over an intensity range and further assessed using the functional principal component analysis. Comparisons between the single-trial responses for each sound type and each treatment were done by inspecting the scores corresponding to the first two principal components. An analogous analysis was performed on the first derivative of the response functions. We conclude that the functional principal component analysis is capable of differentiating between the controls and salicylate treatments for each type of sound. It also well separates the response function for tones and clicks. The results of linear discriminant analysis show, that scores of the first two principal components are effective cluster predictors. However, the distinction is less pronounced in case the first derivative of the response.

Suggested Citation

  • Jan Koláček & Ondřej Pokora & Daniela Kuruczová & Tzai-Wen Chiu, 2019. "Benefits of functional PCA in the analysis of single-trial auditory evoked potentials," Computational Statistics, Springer, vol. 34(2), pages 617-629, June.
  • Handle: RePEc:spr:compst:v:34:y:2019:i:2:d:10.1007_s00180-018-0819-6
    DOI: 10.1007/s00180-018-0819-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00180-018-0819-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s00180-018-0819-6?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. Germán Aneiros & Ricardo Cao & Philippe Vieu, 2019. "Editorial on the special issue on Functional Data Analysis and Related Topics," Computational Statistics, Springer, vol. 34(2), pages 447-450, June.

    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:spr:compst:v:34:y:2019:i:2:d:10.1007_s00180-018-0819-6. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.