Silvano Pupolin

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About

Silvano Pupolin is a leading researcher in neurorehabilitation engineering, with a primary focus on developing brain-computer interface (BCI) technologies to restore motor function in chronic stroke patients. His major contributions center on the design and validation of closed-loop BCI systems that integrate sensorimotor rhythm detection with contingent force feedback, creating operant learning paradigms to drive neuroplasticity. In his seminal 2013 work (11 citations), Pupolin demonstrated that a closed-loop BCI combining motor imagery with contingent force feedback could significantly improve motor recovery in chronic stroke survivors. He extended this work in a 2014 study (7 citations) specifically targeting arm reaching ability—a fundamental daily living skill—by implementing an operant learning training protocol that reinforced desired neural activity patterns with precise force feedback. These pioneering studies established that even years after stroke, the damaged brain can be retrained through BCI-mediated neurofeedback. Pupolin's research is notable for its rigorous clinical application, moving BCI from laboratory demonstrations to practical rehabilitation tools. His work has been instrumental in showing that contingent, real-time feedback is critical for driving cortical reorganization, and his platforms continue to influence the design of next-generation neurorehabilitation systems.