Devashree Tripathy

Central Electronics Engineering Research Institute, Academy of Scientific and Innovative Research

Papers

Total Citations

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About

Devashree Tripathy is a researcher at the forefront of brain-computer interface (BCI) technology, specializing in the integration of electroencephalography (EEG) with real-time robotic control systems. Her work focuses on translating neural signals into actionable commands for assistive and autonomous devices, with a particular emphasis on speed-controllable robotic platforms. Tripathy’s most cited paper, “Real-Time BCI System Design to Control Arduino Based Speed Controllable Robot Using EEG” (2018, 11 citations), demonstrates a practical, low-cost framework for decoding brain activity to modulate robotic motion in real time. This builds on her earlier foundational study, “Design and Implementation of Brain Computer Interface Based Robot Motion Control” (2014, 6 citations), which established core methodologies for non-invasive neural control. Together, these contributions have advanced the accessibility of BCI-driven robotics, offering scalable solutions for rehabilitation, assistive technology, and human-machine interaction. Tripathy’s work is notable for bridging the gap between theoretical BCI algorithms and tangible hardware implementation, making her a key figure in applied neural engineering. Her research continues to inspire innovations in real-time signal processing and embedded systems for neural control.