This chapter focus on the different implementations of brain-computer interface (BCI) based on Steady-State Visually Evoked Potentials (SSVEPs). In offline BCI, feature extraction and the classification are performed at the end of the session, when all trials are available. Whereas, in on-line settings, they are performed several times during each trial, usually for each available epoch recorded by the electroencephalogram (EEG) device, enabling real-time and asynchronous BCI. A recent successful approach in feature extraction and signal processing for BCI is Riemannian geometry , which deals with covariance matrices. They capture the degree of correlation between several random variables, that is how the brain signals change relatively to each other. These techniques have demonstrated their benefit on several occasions, leading to winning algorithms in international competitions and to state-of-the-art results on renowned BCI benchmarks. After reviewing some of the most robust approaches in feature extraction for SSVEP, this chapter will introduce newer tools based on Riemannian geometry. With an application to SSVEP, this article shows through a comparison how Riemannian geometry allows one to easily define offline and online implementations that have better accuracies than state of the art.