Section: New Results

EEG signal classification for BCI based on Riemannian geometry

Participants : Fabien Lotte

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. We proposed a review of how these approaches have been used for EEG-based BCI, in particular for feature representation and learning, classifier design and calibration time reduction. Finally, we also identified relevant challenges and promising research directions for EEG signal classification in BCIs, such as feature tracking on manifold or multi-task learning [18].