Channel Selection Procedure using Riemannian distance for BCI applications

Alexandre Barachant (1) , Stephane Bonnet (1)

1 CEA-LETI - Commissariat à l'énergie atomique et aux énergies alternatives - Laboratoire d'Electronique et de Technologie de l'Information

This article describes a new algorithm to select a subset of electrodes in BCI experiments. It is illustrated on a two-class motor imagery paradigm. The proposed approach is based on the Riemannian distance between spatial covariance matrices which allows to indirectly assess the discriminability between classes. Sensor selection is automatically done using a backward elimination principle. The method is tested on the dataset IVa from BCI competition III. The identified subsets are both consistent with neurophysiological principles and effective, achieving optimal performances with a reduced number of channels.

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Alexandre Barachant, Stephane Bonnet. Channel Selection Procedure using Riemannian distance for BCI applications. The 5th International IEEE EMBS Conference on Neural Engineering, Apr 2011, Cancun, Mexico. pp.TBA. ⟨hal-00602707⟩