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

Autor: Jan Koláček, Tzai-Wen Chiu, Daniela Kuruczova, Ondřej Pokora
Rok vydání: 2018
Předmět:
Zdroj: Computational Statistics. 34:617-629
ISSN: 1613-9658
0943-4062
DOI: 10.1007/s00180-018-0819-6
Popis: 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.
Databáze: OpenAIRE
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