Riemannian approaches in Brain-Computer Interfaces: a review

Florian Yger (1) , Maxime Berar (2) , Fabien Lotte (3)

1 LAMSADE - Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision
2 DocApp - LITIS - Equipe Apprentissage
3 Potioc - Popular interaction with 3d content

Although promising from numerous applications, current Brain-Computer Interfaces (BCIs) still suffer from a number of limitations. In particular, they are sensitive to noise, outliers and the non-stationarity of ElectroEncephaloGraphic (EEG) signals, they require long calibration times and are not reliable. Thus, new approaches and tools, notably at the EEG signal processing and classification level, are necessary to address these limitations. Riemannian approaches, spearheaded by the use of covariance matrices, are such a very promising tool slowly adopted by a growing number of researchers. This article, after a quick introduction to Riemannian geometry and a presentation of the BCI-relevant manifolds, reviews how these approaches have been used for EEG-based BCI, in particular for feature representation and learning, classifier design and calibration time reduction. Finally, relevant challenges and promising research directions for EEG signal classification in BCIs are identified, such as feature tracking on manifold or multi-task learning.

Mots clés: Index Terms—Riemannian geometry; Brain-Computer Inter-; face (BCI); covariance matrices; subspaces; source extraction; Electroencephalography (EEG); classification

Domaines: Intelligence artificielle [cs.AI]; Biotechnologie; Apprentissage [cs.LG]; Traitement du signal et de l'image [eess.SP]; Neurosciences

Florian Yger, Maxime Berar, Fabien Lotte. Riemannian approaches in Brain-Computer Interfaces: a review. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2017. ⟨hal-01394253⟩