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About

Neha Sharma is a multidisciplinary researcher whose work sits at the dynamic intersection of brain-computer interfaces (BCIs), human-robot interaction, and intelligent healthcare systems. She has established herself as a notable voice in EEG-based motor imagery (MI) signal analysis, contributing both comprehensive reviews and novel deep learning methodologies to this challenging field. Her most cited work, a 2023 comprehensive review of EEG-based motor imagery recognition (45 citations), systematically maps recent trends and analytical approaches for these inherently noisy, non-stationary signals — a resource that has quickly become a valuable reference for BCI researchers. Complementing this, her deep temporal network framework for MI classification (25 citations) demonstrates her capacity to translate theoretical insights into practical computational solutions. Beyond neural signal processing, Sharma has explored the human dimensions of robotics, examining interaction design through bodystorming methodologies and conducting bibliometric analyses of AI and robotics adoption in the hospitality sector. Her work on IoMT implementation further reflects a broader commitment to leveraging emerging technologies for real-world healthcare transformation. With nearly 90 cumulative citations across publications spanning just a few years, Sharma represents a rapidly rising research presence bridging neurotechnology, robotics, and digital health.