Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Image Feature Extraction by Sparse Coding and Independent Component Analysis
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
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At present, Independent Components Analysis (ICA) represents the most important and efficient approach for extraction of independent non-Gaussian linearly mixed signals. This statistic-informative technique has been successfully applied to fMRI temporal data, which can be considered as an overlapped mixture of hemodynamic signals, physiological perturbations and noise. In this paper an extension to spatial application of ICA (sICA) was performed. The results confirmed that the spatial approach permits to obtain improved identification of brain activities, even when the temporal length of data is reduced.