Self-Organizing Maps
Blind separation methods based on Pearson system and its extensions
Signal Processing
Injecting noise for analysing the stability of ICA components
Signal Processing - Special issue on independent components analysis and beyond
Denoising using local projective subspace methods
Neurocomputing
Clustering of signals using incomplete independent component analysis
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Projection approximation subspace tracking
IEEE Transactions on Signal Processing
ICA, kernel methods and nonnegativity: New paradigms for dynamical component analysis of fMRI data
Engineering Applications of Artificial Intelligence
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We introduce Model-free Toolbox (MFBOX), a Matlab toolbox for analyzing multivariate data sets in an explorative fashion. Its main focus lies on the analysis of functional Nuclear Magnetic Resonance Imaging (fMRI) data sets with various model-free or data-driven techniques. In this context, it can also be used as plugin for SPM5, a popular tool in regression-based fMRI analysis. The toolbox includes BSS algorithms based on various source models including ICA, spatiotemporal ICA, autodecorrelation and NMF. They can all be easily combined with higher-level analysis methods such as reliability analysis using projective clustering of the components, sliding time window analysis or hierarchical decomposition. As an example, we use MFBOX for the analysis of an fMRI experiment and present short comparisons with the SPM results. The MFBOX is freely available for download at http://mfbox.sf.net.