Variational Representational Similarity Analysis (vRSA) for M/EEG

TL;DR

This paper presents vRSA, a Bayesian method for analyzing neural response similarities in M/EEG data, enabling efficient hypothesis testing and uncertainty quantification.

Contribution

It introduces variational RSA for electromagnetic recordings, extending previous MRI-based methods with a Bayesian covariance component approach.

Findings

Allows testing of stimulus similarity in neural data

Provides Bayesian credible intervals for uncertainty

Demonstrated on open EEG dataset

Abstract

This paper introduces variational representational similarity analysis RSA (vRSA) for electromagnetic recordings of neural responses (e.g., EEG, MEG, ECoG or LFP). Variational RSA is a Bayesian approach for testing whether the similarity of stimuli or experimental conditions is expressed in univariate or multivariate neural recordings. Extending an approach previously introduced in the context of functional MRI, vRSA decomposes the condition-by-condition data covariance matrix into hypothesised effects and observation noise, thereby casting RSA as a covariance component estimation problem. In this context, peristimulus time may be treated as an experimental factor, enabling one to test for the probability that different experimental effects are expressed in data at different times. Variational Bayesian methods are used for model estimation and model comparison, which confer a number of advantages over classical approaches, including statistically efficient hypothesis testing, quantification of uncertainty using Bayesian credible intervals and computational efficiency. After introducing the theory, we provide a worked example using openly available EEG data. Software functions implementing vRSA for the SPM software package accompany this paper, together with exemplar analysis scripts.