A MATLAB toolbox for M/EEG classification, proximity matrix construction, and visualization. M/EEG classification involves constructing a statistical model from categorically labeled data observations. Such a model can then be used to predict labels of new observations. Representational Similarity Analysis (RSA) is a paradigm that allows quantitative comparison between stimulus responses across different data modalities (e.g., EEG, behavioral data) by abstracting data from each modality into Representational Dissimilarity Matrices (RDMs) that can be directly compared in a common unit space. Classification is useful for RSA, as pairwise classifier accuracies or multiclass classifier confusions can serve as measures of distance or similarity, respectively, across a stimulus set and can thus be used to construct the RDMs used for RSA.