Brain-machine interfaces (BMIs) are systems able to translate human brain patterns into commands for robotic devices. Despite the flourishing of brain-actuated prototypes, after three decades of research, BMI is still lacking practicable solutions for daily use by end-users. Current research approaches that assume that BMI should be treated only as a decoder tool, have not been able to face this translational challenge. BrainGear (a two-year project, funded by the Dept. of Information Engineering of the University of Padova) radically revolutionizes the traditional approach by reformulating BMI as a multifaceted symbiotic learning entity where the three actors involved —user, decoder, and robotic device— have to mutually learn from each other. Neuroscientific and robotic methodologies are combined in order to create and explicitly promote the mutual learning interactions between each of these actors.