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About

Monalisa Pal is a pioneering researcher at the intersection of brain-computer interfaces (BCI) and robotic control, with a focus on decoding human intent through neural and tactile signals. Her work centers on two key areas: translating brain signals into precise motor commands and enhancing robotic perception through artificial touch. In her highly cited 2014 study (9 citations), Pal developed a method to decode wrist and finger movements from electroencephalography (EEG) signals, enabling real-time control of robotic arms via thought alone—a breakthrough for assistive technologies. She further advanced BCI systems with a 2017 paper introducing a hybrid approach using visual evoked potentials, reducing the need for extensive subject training. Complementing this, Pal’s research on vibrotactile feedback (2016, 9 citations) and texture/deformability-based surface recognition (2016, 8 citations) has empowered robotic arms to convey object shape and material properties to users, bridging sensory gaps. Her work has garnered over 28 citations, reflecting its impact on neuroprosthetics and human-robot interaction. Pal’s contributions are notable for their practical integration of neural decoding and haptic feedback, pushing boundaries in accessible, intuitive robotic control.