On the interpretation of linear Riemannian tangent space model parameters in M/EEG
This work addresses interpretability for researchers in brain-computer interfaces and biomarker development, though it is incremental as it builds on existing Riemannian methods.
The paper tackled the limited interpretability of Riemannian tangent space models in M/EEG applications by proposing a method to transform model parameters into interpretable patterns, demonstrating its validity in simulations and real datasets.
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.