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A multi-variate blind source separation algorithm

Goldhacker, M. , Keck, P., Igel, A., Lang, E.W. and Tomé, A.M. (2017) A multi-variate blind source separation algorithm. Computer Methods and Programs in Biomedicine 151, pp. 91-99.

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Other URL: http://doi.org/10.1016/j.cmpb.2017.08.019


Abstract

Background and objective: The study follows the proposal of decomposing a given data matrix into a product of independent spatial and temporal component matrices. A multi-variate decomposition approach is presented, based on an approximate diagonalization of a set of matrices computed using a latent space representation. Methods: The proposed methodology follows an algebraic approach, which is ...

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Item type:Article
Date:2017
Institutions:Psychology and Pedagogy > Institut für Psychologie
Psychology and Pedagogy > Institut für Psychologie > Lehrstuhl für Psychologie I (Allgemeine Psychologie I und Methodenlehre) - Prof. Dr. Mark W. Greenlee
Identification Number:
ValueType
10.1016/j.cmpb.2017.08.019DOI
Keywords:INDEPENDENT COMPONENT ANALYSIS; HUMAN CONNECTOME PROJECT; BRAIN CONNECTIVITY; FMRI; NETWORKS; ROBUST; MODES; ICA; Blind source separation; Independent component analysis; fMRI; Resting state; Retinotopy; Spatio temporal
Dewey Decimal Classification:100 Philosophy & psychology > 150 Psychology
Status:Published
Refereed:Yes, this version has been refereed
Created at the University of Regensburg:Yes
Item ID:39650
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