Blind source separation for fmri signals using spatial independent component analysis

Abstract

The analysis of functional magnetic resonance imaging (fMRI) signals using independent component analysis (ICA) has been a hotspot in the recent years. The main principle of performing the blind source separation (BSS) using spatial independent component analysis (SICA) is described. Since the fMRI signals from the experiments can be seen as a specific problem of BSS, the FastICA can be used to realize the BSS. Current analytical techniques, which are applied to fMRI signals, require a prior knowledge or specific assumptions about the time courses of processes contributing to the measured signals. Without any prior knowledge about the time courses of processes contributing to the measured signals, only the FastICA can perform BSS and be used to separate fMRI signals into task-related independent components, head movement independent components, transient task-related independent components, noisy independent components and other independent component signals.