Brain-computer interfaces (BCIs) are becaming more available to the general public, and have already been used to control applications such as computer games. One disadvantage is that they are not completely reliable. In order to increase BCI performances, some low-level adjustments can be made, such as signal processing, as well as high level adjustments such as modifying the controller paradigm. In this study, we explore a novel, context-dependant, approach for a steady-state visual-evoked potential (SSVEP)-based BCI controller. This controller uses two kinds of behavior alternation: commands can be added and removed if their use is irrelevant to the context and the actions resulting from their activation can be weighted depending on the likeliness of the actual intention of the user. This controller has been integrated within a BCI computer game and its influence in performance and mental workload has been addressed through a pilot experiment. Preliminary results have shown a workload reduction and performance improvement with the context-dependent controller, while keeping the engagement levels untouched.