Interpretable Riemannian Classification in Brain-Computer Interfacing

Riemannian methods are currently one of the best ways of building classifiers for EEG data in a brain-computer interface (BCI). However, they are computationally complex and suffer from a lack of interpretability. Since the full covariance matrix is used for each classification, it is not immediately possible to see what underlying signals are generating the classified changes in variance. Particularly in a rehabilitation context, where it is essential to control which brain signals are used for classification, this can be a severely limiting factor. Further, the requirement to perform a matrix logarithm can become prohibitively complex for real-time computation. In this work, we explore a method for extracting spatial filters from a solution in the Riemannian tangent space and compare it against common spatial patterns. We show via comparisons on multiple open-access datasets that it is possible to generate filters that approach the performance of the full Riemannian solution while maintaining interpretability.