Papers
5
Total Citations
71
H-Index
4
About
Giuseppe Lisi is a leading researcher at the intersection of brain-machine interfaces (BMIs), assistive robotics, and human motor control. His work focuses on decoding neural signals—particularly event-related desynchronization (ERD/ERS) from EEG—to create intuitive, plug-and-play brain-robot interfaces. A key contribution is his development of a dry-wireless EEG headset combined with asynchronous adaptive feature extraction, enabling a co-adaptive BMI that triggers lower limb exoskeletons via foot motor imagery (20 citations). He has also investigated how afferent input from robotic leg assistance influences BMI decoding performance (29 citations), advancing real-world rehabilitation applications. Beyond neural interfaces, Lisi explores the sensory-motor foundations of social coordination, using haptic interactions to study how leader-follower relationships are acquired (12 citations). His work on Bayesian estimation of robot-guided training performance (3 citations) further demonstrates his commitment to optimizing human-robot collaboration. With over 70 total citations, Lisi’s research bridges engineering and neuroscience, pushing toward practical, noninvasive BMIs for assistive and rehabilitation robotics.
Research Focus
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Top Papers
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