Abstract
Author summary In neuroscience, researchers are often interested in the modulations of specific signal components (e.g., oscillations in a particular frequency band), that have to be extracted from a background of both rhythmic and non-rhythmic activity. As the interfering background signals often have higher amplitude than the component of interest, it is crucial to develop methods that are able to perform some sort of signal decomposition. In this paper, we introduce a Bayesian decomposition method that exploits a prior dynamical model of the neural temporal dynamics in order to extract signal components with well-defined dynamic features. The method is based on Gaussian process regression with prior distributions determined by the covariance functions of linear stochastic differential equations. Using simulations and analysis of real MEG data, we show that these informed prior distributions allow for the extraction of interpretable dynamic components and the estimation of relevant signal modulations. We generalize the method to the analysis of spatiotemporal cortical activity and show that the framework is intimately related to well-established source-reconstruction techniques.
