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About

Arnab Rakshit is a leading researcher in the field of Brain-Computer Interfaces (BCI) for assistive robotics, with a focus on restoring motor function to individuals with severe motor disabilities. His work centers on developing hybrid BCI systems that translate neural signals into precise, real-time control of robotic arms, addressing critical limitations in existing open-loop control schemes. Rakshit’s most impactful contribution, his 2020 paper on a hybrid BCI for closed-loop position control of a robot arm (59 citations), demonstrates a novel approach that allows users to mentally guide a robot arm with enhanced accuracy and tracking, a significant leap over prior methods. His 2023 work on autonomous grasping of 3-D objects using vision-actuated BCI (12 citations) further advances the field by integrating computer vision for object recognition and manipulation. Earlier studies, including his 2016 paper on robotic link position control (4 citations) and his 2017 hybrid BCI using visual evoked potentials (2 citations), laid the groundwork by tackling the challenge of rigorous subject training. Rakshit’s cumulative work, with over 77 citations, has established him as a key innovator in making BCI-controlled robotics more practical, intuitive, and accessible for real-world assistive applications.