Real-time workload estimation can be an important tool to improve human-machine interaction. Despite multiple efforts in this sense, most studies focused on tasks designed to have extreme conditions of workload (high vs low), and these levels remain rather constant throughout task execution. However, some applications may induce changing levels of workload and it is not clear how decoders trained in the extreme conditions will perform. In this study we study this scenario in a simulated drone task where participants had to fly through several waypoints. The task difficulty was modulated by the size of waypoints; in one of the studied conditions, the size was modulated in real time by the EEG-based decoding of the perceived difficulty. We show that this protocol can effectively induce different levels of workload and perceived difficulty. Furthermore, post-experiment analysis using a sparse-regularized technique supports the feasibility of decoding perceived difficulty in this dynamically changing task above chance level (average class-balanced accuracy across subjects 67% +/- 7%). Interestingly, an analysis of the selected features in independent recording sessions showed that roughly 20% of the features were stable across multiple sessions.