  Riemannian tangent space methods offer state-of-the-art performance in
magnetoencephalography (MEG) and electroencephalography (EEG) based
applications such as brain-computer interfaces and biomarker development. One
limitation, particularly relevant for biomarker development, is limited model
interpretability compared to established component-based methods. Here, we
propose a method to transform the parameters of linear tangent space models
into interpretable patterns. Using typical assumptions, we show that this
approach identifies the true patterns of latent sources, encoding a target
signal. In simulations and two real MEG and EEG datasets, we demonstrate the
validity of the proposed approach and investigate its behavior when the model
assumptions are violated. Our results confirm that Riemannian tangent space
methods are robust to differences in the source patterns across observations.
We found that this robustness property also transfers to the associated
patterns.
