Feature selection for decoding of cognitive states in multiple-subject functional magnetic resonance imaging data

Abstract

The last two decades have seen a surge in the interest in research based on Functional Magnetic Resonance Imaging (fMRI) data. Decoding of cognitive states based on fMRI activation profiles has become a very active topic in this area. fMRI data is very high dimensional and noisy. However, there is a dearth of datasets to work on. Sharing of learning by analyzing datasets drawn across multiple subjects in an experiment can help in increasing the amount of data we have for analysis. Decoding of cognitive states using classifiers trained across multiple subjects is a challenging task because of differences in anatomy and cognition. Selecting features to analyze from the dataset is a key step in the analysis of fMRI data. In this paper we apply PCA, ICA and five non-linear dimensionality reduction techniques to the fMRI data. The aim of this work is to analyze which technique can provide the best feature selection to capture the commonality across multiple subjects. The reduced datasets are then used to train classifiers to solve a multiple-subject decoding problem. © 2013 Springer.