Brain-Computer Interfaces (BCIs) are systems that can translate brain activity patterns of a user into messages or commands for an interactive application. Such brain activity is typically measured using Electroencephalography (EEG), before being processed and classified by the system. EEG-based BCIs have proven promising for a wide range of applications ranging from communication and control for motor impaired users, to gaming targeted at the general public, real-time mental state monitoring and stroke rehabilitation, to name a few. Despite this promising potential, BCIs are still scarcely used outside laboratories for practical applications. The main reason preventing EEG-based BCIs from being widely used is arguably their poor usability, which is notably due to their low robustness and reliability, as well as their long training times. In this talk I present some of our research aimed at addressing these points in order to make EEG-based BCIs usable, i.e., to increase their efficacy and efficiency. In particular, I will present a set of contributions towards this goal 1) at the user training level, to ensure that users can learn to control a BCI efficiently and effectively, and 2) at the usage level, to explore novel applications of BCIs for which the current reliability can already be useful, e.g., for neuroergonomics or real-time brain activity and mental state visualization.