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This paper presents a new classification methods for Event Related Potentials(ERP) based on an Information geometry framework. Through a new estimation ofcovariance matrices, this work extend the use of Riemannian geometry, which waspreviously limited to SMR-based BCI, to the problem of classification of ERPs.As compared to the state-of-the-art, this new method increases performance,reduces the number of data needed for the calibration and features goodgeneralisation across sessions and subjects. This method is illustrated on datarecorded with the P300-based game brain invaders. Finally, an online andadaptive implementation is described, where the BCI is initialized with genericparameters derived from a database and continuously adapt to the individual,allowing the user to play the game without any calibration while keeping a highaccuracy.

A Plug&Play P300 BCI Using Information Geometry