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In brain–computer interfaces (BCI), most of the approaches based on event-related potential (ERP) focus on the detection of P300, aiming for single trial classification for a speller task, but leaving the character classification out of the scope. To reduce the number of repetitions while maintaining a good character classification, it is critical to embrace the full classification problem. We introduce an end-to-end pipeline, starting from feature extraction, and composed of an ERP-level classification using probabilistic Riemannian MDM which feeds a character-level classification using Bayesian accumulation of confidence across trials. Whereas existing approaches only increase the confidence of a character when it is flashed, our new pipeline, called B a ye s ian  a ccumulation of Riemannian  p robabilities (ASAP), update the confidence of each character after each flash. We demonstrate that this seamless processing of information from signal to BCI characters performs significantly better than standard methods on public P300 datasets.

End-to-end P300 BCI using Bayesian accumulation of Riemannian probabilities