  Adaptive Brain-Computer interfaces (BCIs) have shown to improve performance,
however a general and flexible framework to implement adaptive features is
still lacking. We appeal to a generic Bayesian approach, called Active
Inference (AI), to infer user's intentions or states and act in a way that
optimizes performance. In realistic P300-speller simulations, AI outperforms
traditional algorithms with an increase in bit rate between 18% and 59%, while
offering a possibility of unifying various adaptive implementations within one
generic framework.
