Sensor-level maps with the kernel two-sample test

Abstract

Traditional approaches to create sensor-level maps from magnetoencephalographic (MEG) data rely on mass-univariate methods. In order to overcome some limitations of univariate approaches, multivariate approaches have been widely investigated, mostly based on the paradigm of classification. Recently a multivariate two-sample test called kernel two-sample test (KTST) has been proposed as an alternative to classification-based methods. Unfortunately the KTST lacks methods for neuroscientific interpretation of its result, e.g. in terms of sensor-level maps. In this work, we address this issue and we propose a cluster-based permutation kernel two-sample test (CBPKTST) to create sensor-level maps. Moreover we propose a procedure that massively reduces the computation so that maps can be produced in minutes. We report preliminary experiments on MEG data in which we show that the proposed procedure has much greater sensitivity than the traditional cluster-based permutation t-test.