  Correlated component analysis as proposed by Dmochowski et al. (2012) is a
tool for investigating brain process similarity in the responses to multiple
views of a given stimulus. Correlated components are identified under the
assumption that the involved spatial networks are identical. Here we propose a
hierarchical probabilistic model that can infer the level of universality in
such multi-view data, from completely unrelated representations, corresponding
to canonical correlation analysis, to identical representations as in
correlated component analysis. This new model, which we denote Bayesian
correlated component analysis, evaluates favourably against three relevant
algorithms in simulated data. A well-established benchmark EEG dataset is used
to further validate the new model and infer the variability of spatial
representations across multiple subjects.
