  Riemannian geometry has been applied to Brain Computer Interface (BCI) for
brain signals classification yielding promising results. Studying
electroencephalographic (EEG) signals from their associated covariance matrices
allows a mitigation of common sources of variability (electronic, electrical,
biological) by constructing a representation which is invariant to these
perturbations. While working in Euclidean space with covariance matrices is
known to be error-prone, one might take advantage of algorithmic advances in
information geometry and matrix manifold to implement methods for Symmetric
Positive-Definite (SPD) matrices. This paper proposes a comprehensive review of
the actual tools of information geometry and how they could be applied on
covariance matrices of EEG. In practice, covariance matrices should be
estimated, thus a thorough study of all estimators is conducted on real EEG
dataset. As a main contribution, this paper proposes an online implementation
of a classifier in the Riemannian space and its subsequent assessment in
Steady-State Visually Evoked Potential (SSVEP) experimentations.
