MVPAlab: A Machine Learning decoding toolbox for multidimensional electroencephalography data

Abstract

MVPAlab is a MATLAB-based and very flexible decoding toolbox for multidimensional electroencephalography and mag-netoencephalography data. The MVPAlab Toolbox implements several machine learning algorithms to compute multivari-ate pattern analyses, cross-classification, temporal generalization matrices and feature and frequency contribution anal-yses. It also provides access to an extensive set of preprocessing routines for, among others, data normalization, data smoothing, dimensionality reduction and supertrials generation. To draw statistical inferences at the group level, MVPAlab includes a non-parametric cluster-based permutation approach. This toolbox has been designed to include an easy-to-use and very intuitive graphic user interface and data representation software, which makes MVPAlab a very convenient tool for those users with few or no previous coding experience. However, MVPAlab is not for beginners only, as it implements several high and low-level routines allowing more experienced users to design their own projects in a highly flexible manner.