Blind Source Separation of Multiple Signal Sources of fMRI Data Sets Using Independent Component Analysis

Blind source separation of multiple signal sources of fMRI data sets using independent component analysis.

PURPOSE The objective of this study was to separate multiple signal components present in functional MRI (fMRI) data sets. Blind source separation techniques were applied to the analysis of fMRI data to determine multiple physiologically relevant independent signal sources. METHOD Computer simulations were performed to test the reliability and robustness of the independent component analysis ...

Independent component analysis and blind source separation

Independent Component Analysis and Blind Source Separation

Blind source separation and independent component analysis

This special issue of Neurocomputing contains a selection of eleven papers about blind source separation and independent component analysis. The papers represent improved and extended versions of contributed papers presented at the ICA 2004 meeting held in Granada, Spain. A total of 15 manuscripts have been suggested for the special issue by the referees of the contributions submitted to the co...

Independent component analysis of nondeterministic fMRI signal sources.

Neuronal activation can be separated from other signal sources of functional magnetic resonance imaging (fMRI) data by using independent component analysis (ICA). Without deliberate neuronal activity of the brain cortex, the fMRI signal is a stochastic sum of various physiological and artifact related signal sources. The ability of spatial-domain ICA to separate spontaneous physiological signal...