Performance of blind source separation algorithms for fMRI analysis using a group ICA method
Independent component analysis (ICA) is a popular blind source separation technique that
has proven to be promising for the analysis of functional magnetic resonance imaging (fMRI) …
has proven to be promising for the analysis of functional magnetic resonance imaging (fMRI) …
Independent component analysis involving autocorrelated sources with an application to functional magnetic resonance imaging
Independent component analysis (ICA) is an effective data-driven method for blind source
separation. It has been successfully applied to separate source signals of interest from their …
separation. It has been successfully applied to separate source signals of interest from their …
Semi-blind ICA of fMRI: a method for utilizing hypothesis-derived time courses in a spatial ICA analysis
Independent component analysis (ICA) is a data-driven approach utilizing high-order
statistical moments to find maximally independent sources that has found fruitful application …
statistical moments to find maximally independent sources that has found fruitful application …
Joint blind source separation by multiset canonical correlation analysis
In this paper, we introduce a simple and effective scheme to achieve joint blind source
separation (BSS) of multiple datasets using multiset canonical correlation analysis (M …
separation (BSS) of multiple datasets using multiset canonical correlation analysis (M …
Unmixing fMRI with independent component analysis
VD Calhoun, T Adali - IEEE Engineering in Medicine and …, 2006 - ieeexplore.ieee.org
Independent component analysis (ICA) is a statistical method used to discover hidden
factors (sources or features) from a set of measurements or observed data such that the …
factors (sources or features) from a set of measurements or observed data such that the …
A novel approach for assessing reliability of ICA for FMRI analysis
W Du, S Ma, GS Fu, VD Calhoun… - 2014 IEEE international …, 2014 - ieeexplore.ieee.org
Independent component analysis (ICA) has proven quite useful for the analysis of functional
magnetic resonance imaging (fMRI) data. However, stability of ICA decompositions is an …
magnetic resonance imaging (fMRI) data. However, stability of ICA decompositions is an …
Comparison of blind source separation algorithms for FMRI using a new Matlab toolbox: GIFT
We study the performance of five blind source separation (BSS) algorithms when applied to
analysis of functional magnetic resonance imaging (fMRI) data. We introduce a Matlab …
analysis of functional magnetic resonance imaging (fMRI) data. We introduce a Matlab …
Partner‐matching for the automated identification of reproducible ICA components from fMRI datasets: Algorithm and validation
Z Wang, BS Peterson - Human brain mapping, 2008 - Wiley Online Library
The analysis of functional magnetic resonance imaging (fMRI) data is complicated by the
presence of a mixture of many sources of signal and noise. Independent component …
presence of a mixture of many sources of signal and noise. Independent component …
Application of independent component analysis with adaptive density model to complex-valued fMRI data
Independent component analysis (ICA) has proven quite useful for the analysis of real world
datasets such as functional resonance magnetic imaging (fMRI) data, where the underlying …
datasets such as functional resonance magnetic imaging (fMRI) data, where the underlying …
Ranking and averaging independent component analysis by reproducibility (RAICAR)
Independent component analysis (ICA) is a data‐driven approach that has exhibited great
utility for functional magnetic resonance imaging (fMRI). Standard ICA implementations …
utility for functional magnetic resonance imaging (fMRI). Standard ICA implementations …