This lecture proposes an accessible introduction to the design of Brain-Computer Interfaces (BCI) based on oscillatory EEG activity (e.g., motor imagery), notably from a signal processing point of view. In particular, it first presents the basic feature extraction and classification tools to design such a BCI. The lecture then describes the use of spatial filters, both simple static ones (e.g., Laplacian) as well as advanced supervised ones (e.g., Common Spatial Patterns and variants) to enhance the performance and the robustness of the whole BCI. A few supervised temporal filters will be considered as well. Alternative EEG features representation are then exposed as promising additions to basic features. This notably includes features measuring the EEG signals complexity, and more importantly, features measuring how EEG signals from different brain areas are synchronized. This lecture will ends by briefly showing the audience that designing oscillatory activity-based BCI is not all about signal processing. Indeed, considering the user and how to train him/her to control the BCI is also a key point for successful BCI design.