Journal Article

Multivariate pattern analysis for MEG: A comparison of dissimilarity measures

Sterzer, Philipp, Cichy, Radoslaw Martin, Guggenmos, Matthias

2018

Overview

Multivariate pattern analysis (MVPA) methods such as decoding and representational similarity analysis (RSA) are growing rapidly in popularity for the analysis of magnetoencephalography (MEG) data. However, little is known about the relative performance and characteristics of the specific dissimilarity measures used to describe differences between evoked activation patterns. Here we used a multisession MEG data set to qualitatively characterize a range of dissimilarity measures and to quantitatively compare them with respect to decoding accuracy (for decoding) and between-session reliability of representational dissimilarity matrices (for RSA). We tested dissimilarity measures from a range of classifiers (Linear Discriminant Analysis – LDA, Support Vector Machine – SVM, Weighted Robust Distance – WeiRD, Gaussian Naïve Bayes – GNB) and distances (Euclidean distance, Pearson correlation). In addition, we evaluated three key processing choices: 1) preprocessing (noise normalisation, removal of the pattern mean), 2) weighting decoding accuracies by decision values, and 3) computing distances in three different partitioning schemes (non-cross-validated, cross-validated, within-class-corrected). Four main conclusions emerged from our results. First, appropriate multivariate noise normalization substantially improved decoding accuracies and the reliability of dissimilarity measures. Second, LDA, SVM and WeiRD yielded high peak decoding accuracies and nearly identical time courses. Third, while using decoding accuracies for RSA was markedly less reliable than continuous distances, this disadvantage was ameliorated by decision-value-weighting of decoding accuracies. Fourth, the cross-validated Euclidean distance provided unbiased distance estimates and highly replicable representational dissimilarity matrices. Overall, we strongly advise the use of multivariate noise normalisation as a general preprocessing step, recommend LDA, SVM and WeiRD as classifiers for decoding and highlight the cross-validated Euclidean distance as a reliable and unbiased default choice for RSA. •We provide Python and MATLAB tutorials for key analysis steps.•We compared dissimilarity measures and preprocessing choices for MEG MVPA.•Multivariate noise normalisation is a key preprocessing step.•LDA, SVM and WeiRD are recommended classifiers for decoding.•The cross-validated Euclidean distance is a reliable and unbiased choice for RSA.

• Publisher: Elsevier Inc,Elsevier Limited
• Subject: Accuracy / Bayesian analysis / Brain - physiology / Brain Mapping - methods / Brain research / Cross-validation / Datasets / Decoding / Discriminant analysis / EEG / Humans / Machine learning / Magnetoencephalography / Magnetoencephalography - methods / MEG / Multi-voxel pattern analysis / Multivariate Analysis / Noise / Noise normalisation / Principal components analysis / Representational similarity analysis / Signal Processing, Computer-Assisted