In applying mental imagery brain-computer interfaces (BCIs) to end users, training is a key part for novice users to get control. In general learning situations, it is an established concept that a trainer assists a trainee to improve his/her aptitude in certain skills. In this work, we want to evaluate whether we can apply this concept in the context of event-related desynchronization (ERD) based, adaptive, hybrid BCIs. Hence, in a first session we merged the features of a high aptitude BCI user, a trainer, and a novice user, the trainee, in a closed-loop BCI feedback task and automatically adapted the classifier over time. In a second session the trainees operated the system unassisted. Twelve healthy participants ran through this protocol. Along with the trainer, the trainees achieved a very high overall peak accuracy of 95.3 %. In the second session, where users operated the BCI unassisted, they still achieved a high overall peak accuracy of 83.6%. Ten of twelve first time BCI users successfully achieved significantly better than chance accuracy. Concluding, we can say that this trainer-trainee approach is very promising. Future research should investigate, whether this approach is superior to conventional training approaches. This trainer-trainee concept could have potential for future application of BCIs to end users.