KOLÁČEK, Jan, Ondřej POKORA, Daniela KURUCZOVÁ and Tzai-Wen CHIU. Benefits of functional PCA in the analysis of single-trial auditory evoked potentials. Computational Statistics. Germany: Springer, 2019, vol. 34, No 2, p. 617-629. ISSN 0943-4062. Available from: https://dx.doi.org/10.1007/s00180-018-0819-6.
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Basic information
Original name Benefits of functional PCA in the analysis of single-trial auditory evoked potentials
Authors KOLÁČEK, Jan (203 Czech Republic, guarantor, belonging to the institution), Ondřej POKORA (203 Czech Republic, belonging to the institution), Daniela KURUCZOVÁ (703 Slovakia, belonging to the institution) and Tzai-Wen CHIU (702 Singapore).
Edition Computational Statistics, Germany, Springer, 2019, 0943-4062.
Other information
Original language English
Type of outcome Article in a journal
Field of Study 10103 Statistics and probability
Country of publisher Germany
Confidentiality degree is not subject to a state or trade secret
WWW Full Text
Impact factor Impact factor: 0.744
RIV identification code RIV/00216224:14310/19:00107161
Organization unit Faculty of Science
Doi http://dx.doi.org/10.1007/s00180-018-0819-6
UT WoS 000467230100010
Keywords (in Czech) funkcionální data, analýza hlavních komponent
Keywords in English Functional data; Principal component analysis; single-trial auditory response
Tags rivok
Tags International impact, Reviewed
Changed by Changed by: Mgr. Marie Šípková, DiS., učo 437722. Changed: 10/3/2020 11:06.
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.
Links
GA15-06991S, research and development projectName: Analýza funkcionálních dat a související témata
Investor: Czech Science Foundation
MUNI/A/1503/2018, interní kód MUName: Matematické statistické modelování 3 (Acronym: MaStaMo3)
Investor: Masaryk University, Category A
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