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Riemannian tangent space methods offer state-of-the-art performance inmagnetoencephalography (MEG) and electroencephalography (EEG) basedapplications such as brain-computer interfaces and biomarker development. Onelimitation, particularly relevant for biomarker development, is limited modelinterpretability compared to established component-based methods. Here, wepropose a method to transform the parameters of linear tangent space modelsinto interpretable patterns. Using typical assumptions, we show that thisapproach identifies the true patterns of latent sources, encoding a targetsignal. In simulations and two real MEG and EEG datasets, we demonstrate thevalidity of the proposed approach and investigate its behavior when the modelassumptions are violated. Our results confirm that Riemannian tangent spacemethods are robust to differences in the source patterns across observations.We found that this robustness property also transfers to the associatedpatterns.

On the interpretation of linear Riemannian tangent space model parameters in M/EEG