Online SSVEP-based BCI using Riemannian geometry

Emmanuel Kalunga (1, 2) , Sylvain Chevallier (1) , Quentin Barthélemy (3) , Karim Djouani (2) , Eric Monacelli (1) , Yskandar Hamam (2)

1 LISV - Laboratoire d'Ingénierie des Systèmes de Versailles
2 TUT - Tshwane University of Technology [Pretoria]
3 Mensia Technologies [Paris]

Challenges for the next generation of Brain Computer Interfaces (BCI) are to mitigate the common sources of variability (electronic, electrical, biological) and to develop online and adaptive systems following the evolution of the subject's brain waves. Studying electroencephalographic (EEG) signals from their associated covariance matrices allows the construction of a representation which is invariant to extrinsic perturbations. As covariance matrices should be estimated, this paper first presents a thorough study of all estimators conducted on real EEG recording. Working in Euclidean space with covariance matrices is known to be error-prone, one might take advantage of algorithmic advances in Riemannian geometry and matrix manifold to implement methods for Symmetric Positive-Definite (SPD) matrices. Nonetheless, existing classification algorithms in Riemannian spaces are designed for offline analysis. We propose a novel algorithm for online and asynchronous processing of brain signals, borrowing principles from semi-unsupervised approaches and following a dynamic stopping scheme to provide a prediction as soon as possible. The assessment is conducted on real EEG recording: this is the first study on Steady-State Visually Evoked Potential (SSVEP) experimentations to exploit online classification based on Rie-mannian geometry. The proposed online algorithm is evaluated and compared with state-of-the-art SSVEP methods, which are based on Canonical Correlation Analysis (CCA). It is shown to improve both the classification accuracy and the information transfer rate in the online and asynchronous setup.

Mots clés: Riemannian Geometry; Online; Asynchronous; Brain-Computer Interfaces; Steady State Visually-Evoked Potential

Domaines: Réseau de neurones [cs.NE]; Apprentissage [cs.LG]; Traitement du signal et de l'image [eess.SP]; Neurosciences

Emmanuel Kalunga, Sylvain Chevallier, Quentin Barthélemy, Karim Djouani, Eric Monacelli, et al.. Online SSVEP-based BCI using Riemannian geometry. Neurocomputing, 2016, 191, pp.55-68. ⟨10.1016/j.neucom.2016.01.007⟩. ⟨hal-01351623⟩