Paper Info
Title
Towards Identification and Characterisation of Selective fMRI Feature Sets Using Independent Component Analysis.
Abstract
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.
Year
DOI
Venue
2012
10.1109/PRNI.2012.15
PRNI
Keywords
Field
DocType
towards identification,multi-voxel pattern analysis,popular technique,brain activity,pattern-information fmri,independent component analysis,diverse set,selective voxels,multivariate technique,primary cognitive,aforementioned set,integrated circuits,pattern analysis,analysis of variance,object recognition,accuracy,feature selection
Voxel,Object detection,Pattern recognition,Feature selection,Multivariate statistics,Computer science,Pattern analysis,Brain activity and meditation,Independent component analysis,Artificial intelligence,Cognition
Conference
Citations 
PageRank 
References 
1
0.43
4
Authors
2
Name
Order
Citations
PageRank
Loizos Markides110.77
Duncan Fyfe Gillies29717.86