Over recent years many algorithms have been used for the analysis of electro-and magnetoencephalograms, assuming a linear model for the mixing of cortical activity at the sensor plane. Such linearity can be theoretically justified, through the Maxwell equations. In this paper we exploit the adaptive and modular nature of the variational Bayesian hierarchical nonlinear factor analysis to give empirical evidence of linearity, as well as to estimate the intrinsic dimension of the generative source space.