Dimension reduction for individual ICA to decompose FMRI during real-world experiences: Principal component analysis vs. canonical correlation analysis

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

Standard

Dimension reduction for individual ICA to decompose FMRI during real-world experiences : Principal component analysis vs. canonical correlation analysis. / Tsatsishvili, Valeri; Cong, Fengyu; Puoliväli, Tuomas; Alluri, Vinoo; Toiviainen, Petri; Nandi, Asoke K.; Brattico, Elvira; Ristaniemi, Tapani.

ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. 2013. p. 137-142.

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

Harvard

Tsatsishvili, V, Cong, F, Puoliväli, T, Alluri, V, Toiviainen, P, Nandi, AK, Brattico, E & Ristaniemi, T 2013, Dimension reduction for individual ICA to decompose FMRI during real-world experiences: Principal component analysis vs. canonical correlation analysis. in ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. pp. 137-142, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013, Bruges, Belgium, 24/04/2013.

APA

Tsatsishvili, V., Cong, F., Puoliväli, T., Alluri, V., Toiviainen, P., Nandi, A. K., Brattico, E., & Ristaniemi, T. (2013). Dimension reduction for individual ICA to decompose FMRI during real-world experiences: Principal component analysis vs. canonical correlation analysis. In ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 137-142)

CBE

Tsatsishvili V, Cong F, Puoliväli T, Alluri V, Toiviainen P, Nandi AK, Brattico E, Ristaniemi T. 2013. Dimension reduction for individual ICA to decompose FMRI during real-world experiences: Principal component analysis vs. canonical correlation analysis. In ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. pp. 137-142.

MLA

Tsatsishvili, Valeri et al. "Dimension reduction for individual ICA to decompose FMRI during real-world experiences: Principal component analysis vs. canonical correlation analysis". ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. 2013, 137-142.

Vancouver

Tsatsishvili V, Cong F, Puoliväli T, Alluri V, Toiviainen P, Nandi AK et al. Dimension reduction for individual ICA to decompose FMRI during real-world experiences: Principal component analysis vs. canonical correlation analysis. In ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. 2013. p. 137-142

Author

Tsatsishvili, Valeri ; Cong, Fengyu ; Puoliväli, Tuomas ; Alluri, Vinoo ; Toiviainen, Petri ; Nandi, Asoke K. ; Brattico, Elvira ; Ristaniemi, Tapani. / Dimension reduction for individual ICA to decompose FMRI during real-world experiences : Principal component analysis vs. canonical correlation analysis. ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. 2013. pp. 137-142

Bibtex

@inproceedings{0939d8e448a74f8b8b444451416b1c49,
title = "Dimension reduction for individual ICA to decompose FMRI during real-world experiences: Principal component analysis vs. canonical correlation analysis",
abstract = "Group independent component analysis (ICA) with special assumptions is often used for analyzing functional magnetic resonance imaging (fMRI) data. Before ICA, dimension reduction is applied to separate signal and noise subspaces. For analyzing noisy fMRI data of individual participants in free-listening to naturalistic and long music, we applied individual ICA and therefore avoided the assumptions of Group ICA. We also compared principal component analysis (PCA) and canonical correlation analysis (CCA) for dimension reduction of such fMRI data. We found interesting brain activity associated with music across majority of participants, and found that PCA and CCA were comparable for dimension reduction.",
author = "Valeri Tsatsishvili and Fengyu Cong and Tuomas Puoliv{\"a}li and Vinoo Alluri and Petri Toiviainen and Nandi, {Asoke K.} and Elvira Brattico and Tapani Ristaniemi",
year = "2013",
language = "English",
isbn = "9782874190810",
pages = "137--142",
booktitle = "ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning",
note = "21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013 ; Conference date: 24-04-2013 Through 26-04-2013",

}

RIS

TY - GEN

T1 - Dimension reduction for individual ICA to decompose FMRI during real-world experiences

T2 - 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013

AU - Tsatsishvili, Valeri

AU - Cong, Fengyu

AU - Puoliväli, Tuomas

AU - Alluri, Vinoo

AU - Toiviainen, Petri

AU - Nandi, Asoke K.

AU - Brattico, Elvira

AU - Ristaniemi, Tapani

PY - 2013

Y1 - 2013

N2 - Group independent component analysis (ICA) with special assumptions is often used for analyzing functional magnetic resonance imaging (fMRI) data. Before ICA, dimension reduction is applied to separate signal and noise subspaces. For analyzing noisy fMRI data of individual participants in free-listening to naturalistic and long music, we applied individual ICA and therefore avoided the assumptions of Group ICA. We also compared principal component analysis (PCA) and canonical correlation analysis (CCA) for dimension reduction of such fMRI data. We found interesting brain activity associated with music across majority of participants, and found that PCA and CCA were comparable for dimension reduction.

AB - Group independent component analysis (ICA) with special assumptions is often used for analyzing functional magnetic resonance imaging (fMRI) data. Before ICA, dimension reduction is applied to separate signal and noise subspaces. For analyzing noisy fMRI data of individual participants in free-listening to naturalistic and long music, we applied individual ICA and therefore avoided the assumptions of Group ICA. We also compared principal component analysis (PCA) and canonical correlation analysis (CCA) for dimension reduction of such fMRI data. We found interesting brain activity associated with music across majority of participants, and found that PCA and CCA were comparable for dimension reduction.

UR - http://www.scopus.com/inward/record.url?scp=84887066666&partnerID=8YFLogxK

M3 - Article in proceedings

AN - SCOPUS:84887066666

SN - 9782874190810

SP - 137

EP - 142

BT - ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Y2 - 24 April 2013 through 26 April 2013

ER -