SparseFastICA for fMRI data analysis

SparseFastICA for fMRI data analysis

As a blind source separation technique, independent component analysis (ICA) has many applications in functional magnetic resonance imaging (fMRI). Certain types of additional prior information, such as the sparsity, have seldom been added to the ICA algorithms as constraints. We proposed a SparseFastICA method by adding the source sparsity as a constraint to the FastICA algorithm to improve the performance of the FastICA. Here is the code of this SparseFastICA method.
This code is modified based on Hugo et al.'s fpica code, please see more details from http://research.ics.aalto.fi/ica/fastica/
Copyright (c) State Key Laboratory of Cognitive Neuroscience and Learning @ Beijing Normal University
Written by Ruiyang Ge
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