In order to design brain-computer interface applications based on steady-state visually evoked potentials (SSVEP), one has to decide on the shape, the attributes and the way the SSVEP stimuli are to be displayed to the user. This choice is generally driven by the tasks to achieve and, eventually, the complexity of the systems to be controlled. An ‘object-affordance centric’ approach consists in flickering objects of interest that appear in the field of view of the user during video feedback, e.g. provided by a robotic surrogate embedded with cameras. Processing of the video images automatically recognizes objects of interest that are in the user’s field of view. These are overlapped with graphical icons that flicker at different frequencies to be recognized by the SSVEP-BCI classifier. This study investigates selected graphical and rendering practices that affect the strength of the SSVEP to produce the highest performance during BCI operations.