2:00 PM - 3:00 PM
Brain-machine interfaces (BMIs) are systems able to translate human brain patterns into commands for robotic devices to restore independence of users suffering from severe motor disabilities. However, despite the flourishing of brain-actuated prototypes, after three decades of research, BMIs are still lacking practicable solutions for daily use by end-users. To date, traditional research approaches assuming that BMI should be treated only as a decoder tool, have not been able to face this translational challenge. During this seminar, I will propose a conceptual shift by considering BMI as a multi-agent learning entity where the three actors involved—user, decoder, and robotic device—must mutually learn from each other in order to achieve robust performances. In this context, the investigation of the learning interactions between the BMI actors becomes fundamental to ensure effective and reliable brain driven neuroprosthetics. Along these lines, I will describe the current and future directions of the research carried out at the Intelligent Autonomous Systems Lab of the Department of Information Engineering of the University of Padua.