Towards Identification and Characterisation of Selective fMRI Feature Sets Using Independent Component Analysis

• Published in: 2012 International Workshop on Pattern Recognition in NeuroImaging pp. 17 - 20
• Main Authors: Markides, L., Gillies, D. F.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2012
• Subjects: Accuracy; Analysis of variance; Brain; feature selection; Independent component analysis; Integrated circuits; multi-voxel pattern analysis; Object recognition; Pattern analysis; pattern-information fMRI
• ISBN: 1467321826; 9781467321822
• DOI: 10.1109/PRNI.2012.15

Pattern-information fMRI uses multivariate techniques for the interpretation of the various patterns that appear in the brain activity. Multi-voxel pattern analysis (MVPA) is a popular technique of pattern-information fMRI which enables the detection of sets of selective voxels that aid in the discrimination between two competing stimuli. Recently researchers have dealt with characterising the aforementioned sets of features by mapping them to primary cognitive processes instead of whole tasks. In this work, we demonstrate how Independent Component Analysis (ICA) provides a promising foundation for both the creation but also the characterisation of diverse sets of selective voxels that can be used later for the prediction of the nature of a given task.