The Kernel Two-Sample Test vs. Brain Decoding

Abstract

Assessing whether the patterns of brain activity systematically differ when the subject is presented with different sets of stimuli is called "brain decoding". The most common solution to this problem is based on testing whether a classifier can accurately predict the type of stimulus from brain data. In this work we present a novel approach to the brain decoding problem which does not require any classifier. The proposed method is based on a high-dimensional two-sample test recently proposed in the machine learning literature. The test tries to determine whether the set of brain recordings related to one kind of stimulus, i.e. the first sample, and the ones related to the other kind of stimulus, i.e. the second sample, are drawn from the same probability distribution or not. In this work we illustrate the advantages of this novel approach together with experimental evidence of its efficacy on magneto encephalographic (MEG) data from a Face, House and Body discrimination task.