A Parcellation Based Nonparametric Algorithm for Independent Component Analysis with Application to fMRI Data

A Parcellation Based Nonparametric Algorithm for Independent Component Analysis with Application to fMRI Data

Independent Component analysis (ICA) is a widely used technique for separating signals that have been mixed together. In this manuscript, we propose a novel ICA algorithm using density estimation and maximum likelihood, where the densities of the signals are estimated via p-spline based histogram sm...

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Bibliographic Details
Main Authors: Shanshan eLi, Shaojie eChen, Chen eYue, Brian eCaffo
Format: Article
Language:English
Published: Frontiers Media S.A. 2016-01-01
Series:Frontiers in Neuroscience
Subjects:
functional MRI
Signal processing
blind source separation
density estimation
P-spline bases
Online Access:http://journal.frontiersin.org/Journal/10.3389/fnins.2016.00015/full

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