A multivariate model for comparison of two datasets and its application to FMRI analysis

Yi Ou Li, Tülay Adali, Vince Daniel Calhoun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this work, we propose a structured approach to compare common and distinct features of two multidimensional datasets using a combination of canonical correlation analysis (CCA) and independent component analysis (TCA). We develop formulations of information theoretic criteria to determine the dimension of the subspaces for common and distinct features of the two datasets. We apply the proposed method to a simulated dataset to demonstrate that it improves the estimation of both common and distinct features when compared to performing ICA on the concatenation of two datasets. We also apply the method to compare brain activation in functional magnetic resonance imaging (fMRI) data acquired during a simulated driving experiment and observe distinctions between the driving and watching conditions revealed in relevant brain function studies.

Original languageEnglish (US)
Title of host publicationMachine Learning for Signal Processing 17 - Proceedings of the 2007 IEEE Signal Processing Society Workshop, MLSP
Pages217-222
Number of pages6
DOIs
StatePublished - 2007
Externally publishedYes
Event17th IEEE International Workshop on Machine Learning for Signal Processing, MLSP-2007 - Thessaloniki, Greece
Duration: Aug 27 2007Aug 29 2007

Other

Other17th IEEE International Workshop on Machine Learning for Signal Processing, MLSP-2007
CountryGreece
CityThessaloniki
Period8/27/078/29/07

Fingerprint

Independent component analysis
Brain
Chemical activation
Experiments
Magnetic Resonance Imaging

ASJC Scopus subject areas

  • Computer Science(all)
  • Signal Processing

Cite this

Li, Y. O., Adali, T., & Calhoun, V. D. (2007). A multivariate model for comparison of two datasets and its application to FMRI analysis. In Machine Learning for Signal Processing 17 - Proceedings of the 2007 IEEE Signal Processing Society Workshop, MLSP (pp. 217-222). [4414309] https://doi.org/10.1109/MLSP.2007.4414309

A multivariate model for comparison of two datasets and its application to FMRI analysis. / Li, Yi Ou; Adali, Tülay; Calhoun, Vince Daniel.

Machine Learning for Signal Processing 17 - Proceedings of the 2007 IEEE Signal Processing Society Workshop, MLSP. 2007. p. 217-222 4414309.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Li, YO, Adali, T & Calhoun, VD 2007, A multivariate model for comparison of two datasets and its application to FMRI analysis. in Machine Learning for Signal Processing 17 - Proceedings of the 2007 IEEE Signal Processing Society Workshop, MLSP., 4414309, pp. 217-222, 17th IEEE International Workshop on Machine Learning for Signal Processing, MLSP-2007, Thessaloniki, Greece, 8/27/07. https://doi.org/10.1109/MLSP.2007.4414309
Li YO, Adali T, Calhoun VD. A multivariate model for comparison of two datasets and its application to FMRI analysis. In Machine Learning for Signal Processing 17 - Proceedings of the 2007 IEEE Signal Processing Society Workshop, MLSP. 2007. p. 217-222. 4414309 https://doi.org/10.1109/MLSP.2007.4414309
Li, Yi Ou ; Adali, Tülay ; Calhoun, Vince Daniel. / A multivariate model for comparison of two datasets and its application to FMRI analysis. Machine Learning for Signal Processing 17 - Proceedings of the 2007 IEEE Signal Processing Society Workshop, MLSP. 2007. pp. 217-222
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