On-Line fMRI Data Classification Using Linear and Ensemble Classifiers

The advent of real-time fMRI pattern classification opens many avenues for interactive self-regulation where the brain's response is better modelled by multivariate, rather than univariate techniques. Here we test three on-line linear classifiers, applied to a real fMRI dataset, collected as part of an experiment on the cortical response to emotional stimuli. We propose a random subspace ensemble as a fast and more accurate alternative to component classifiers. The on-line linear discriminant classifier (O-LDC) was found to be a better base classifier than the on-line versions of the perceptron and the balanced winnow.