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

J 2019

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

KOLÁČEK, Jan, Ondřej POKORA, Daniela KURUCZOVÁ a Tzai-Wen CHIU

Základní údaje

Originální název

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

Autoři

KOLÁČEK, Jan (203 Česká republika, garant, domácí), Ondřej POKORA (203 Česká republika, domácí), Daniela KURUCZOVÁ (703 Slovensko, domácí) a Tzai-Wen CHIU (702 Singapur)

Vydání

Computational Statistics, Germany, Springer, 2019, 0943-4062

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10103 Statistics and probability

Stát vydavatele

Německo

Utajení

není předmětem státního či obchodního tajemství

Odkazy

Full Text

Impakt faktor

Impact factor: 0.744

Kód RIV

RIV/00216224:14310/19:00107161

Organizační jednotka

Přírodovědecká fakulta

DOI

http://dx.doi.org/10.1007/s00180-018-0819-6

UT WoS

000467230100010

Klíčová slova česky

funkcionální data, analýza hlavních komponent

Klíčová slova anglicky

Functional data; Principal component analysis; single-trial auditory response

Štítky

rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 10. 3. 2020 11:06, Mgr. Marie Šípková, DiS.

Anotace

V originále

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.

Návaznosti

GA15-06991S, projekt VaV

Název: Analýza funkcionálních dat a související témata

Investor: Grantová agentura ČR, Analýza funkcionálních dat a související témata

MUNI/A/1503/2018, interní kód MU

Název: Matematické statistické modelování 3 (Akronym: MaStaMo3)

Investor: Masarykova univerzita, Matematické statistické modelování 3, DO R. 2020_Kategorie A - Specifický výzkum - Studentské výzkumné projekty

Přiložené soubory

clanek.pdf

Požádat o autorskou verzi souboru

Citovat

KOLÁČEK, Jan, Ondřej POKORA, Daniela KURUCZOVÁ a Tzai-Wen CHIU. Benefits of functional PCA in the analysis of single-trial auditory evoked potentials. Computational Statistics. Germany: Springer, 2019, roč. 34, č. 2, s. 617-629. ISSN 0943-4062. Dostupné z: https://dx.doi.org/10.1007/s00180-018-0819-6.

@article{1413706,
   author = {Koláček, Jan and Pokora, Ondřej and Kuruczová, Daniela and Chiu, TzaiandWen},
   article_location = {Germany},
   article_number = {2},
   doi = {http://dx.doi.org/10.1007/s00180-018-0819-6},
   keywords = {Functional data; Principal component analysis; single-trial auditory response},
   language = {eng},
   issn = {0943-4062},
   journal = {Computational Statistics},
   title = {Benefits of functional PCA in the analysis of single-trial auditory evoked potentials},
   url = {https://link.springer.com/article/10.1007%2Fs00180-018-0819-6},
   volume = {34},
   year = {2019}
}

TY  - JOUR
ID  - 1413706
AU  - Koláček, Jan - Pokora, Ondřej - Kuruczová, Daniela - Chiu, Tzai-Wen
PY  - 2019
TI  - Benefits of functional PCA in the analysis of single-trial auditory evoked potentials
JF  - Computational Statistics
VL  - 34
IS  - 2
SP  - 617-629
EP  - 617-629
PB  - Springer
SN  - 09434062
KW  - Functional data
KW  - Principal component analysis
KW  - single-trial auditory response
UR  - https://link.springer.com/article/10.1007%2Fs00180-018-0819-6
L2  - https://link.springer.com/article/10.1007%2Fs00180-018-0819-6
N2  - 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.
ER  -

KOLÁČEK, Jan, Ondřej POKORA, Daniela KURUCZOVÁ a Tzai-Wen CHIU. Benefits of functional PCA in the analysis of single-trial auditory evoked potentials. \textit{Computational Statistics}. Germany: Springer, 2019, roč.~34, č.~2, s.~617-629. ISSN~0943-4062. Dostupné z: https://dx.doi.org/10.1007/s00180-018-0819-6.

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