The Riemannian spatial pattern method: mapping and clustering movement imagery using Riemannian geometry
Christelle Larzabal, Vincent Auboiroux, Serpil Karakas, Guillaume Charvet, Alim‐Louis Benabid, Stéphan Chabardès, Thomas Costecalde, Stéphane Bonnet
Abstract
Abstract Objective . Over the last decade, Riemannian geometry has shown promising results for motor imagery classification. However, extracting the underlying spatial features is not as straightforward as for applying common spatial pattern (CSP) filtering prior to classification. In this article, we propose a simple way to extract the spatial patterns obtained from Riemannian classification: the Riemannian spatial pattern (RSP) method, which is based on the backward channel selection procedure. Approach . The RSP method was compared to the CSP approach on ECoG data obtained from a quadriplegic patient while performing imagined movements of arm articulations and fingers. Main results. Similar results were found between the RSP and CSP methods for mapping each motor imagery task with activations following the classical somatotopic organization. Clustering obtained by pairwise comparisons of imagined motor movements however, revealed higher differentiation for the RSP method compared to the CSP approach. Importantly, the RSP approach could provide a precise comparison of the imagined finger flexions which added supplementary information to the mapping results. Significance. Our new RSP method illustrates the interest of the Riemannian framework in the spatial domain and as such offers new avenues for the neuroimaging community. This study is part of an ongoing clinical trial registered with ClinicalTrials.gov, NCT02550522.