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

Jan Koláček (), Ondřej Pokora, Daniela Kuruczová and Tzai-Wen Chiu
Additional contact information
Jan Koláček: Masaryk University
Ondřej Pokora: Masaryk University
Daniela Kuruczová: Masaryk University
Tzai-Wen Chiu: Masaryk University

Computational Statistics, 2019, vol. 34, issue 2, No 10, 617-629

Abstract: 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.

Keywords: Functional data; Principal component analysis; Single-trial auditory response (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s00180-018-0819-6 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:compst:v:34:y:2019:i:2:d:10.1007_s00180-018-0819-6

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/180/PS2

DOI: 10.1007/s00180-018-0819-6

Access Statistics for this article

Computational Statistics is currently edited by Wataru Sakamoto, Ricardo Cao and Jürgen Symanzik

More articles in Computational Statistics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().