The application of machine learning approaches to decode cognitive states through functional Magnetic Resonance Imaging (fMRI) is one of the emerging fields of research over the past decade. Multivoxel Pattern Analysis (MVPA) treats the activation of multiple voxels from the fMRI data as a pattern to decode the brain states using machine learning based classifiers. The potential in designing a classifier to accurately classify the discriminating cognitive states has attracted great attention from machine learning researchers. Interest has been evinced in particular to the application of such classifiers to study brain functions, diagnose mental diseases, detect deception and develop a brain-computer-interface. This paper surveys the recent development of machine learning approaches in cognitive state classification and brain activity prediction. Comparative studies of various techniques have been investigated to appreciate their merits and demerits. Furthermore, feature selection is discussed in this survey as an important preprocessing step in MVPA because it incorporates those features that will be integrated in the classification task of fMRI data, thereby improving the performance of the classifier. Features can be selected by restricting the analysis to specific anatomical regions or by computing univariate (voxel-wise) or multivariate statistics. Besides a summary and the future perspective of this field, an extensive list of bibliography is included for the community of interest.
Keywords: Classification, feature selection, feature extraction, functional magnetic resonance imaging, brain activity prediction, machine learning.