Rishi Raj Sharma

Defence Institute of Advanced Technology

Papers

Total Citations

178

H-Index

About

Rishi Raj Sharma is a rising researcher at the intersection of affective computing, brain-computer interfaces, and human-robot interaction. His work focuses on decoding neural and muscular signals to enable more intuitive human-machine collaboration. Sharma’s most influential contribution is his 2022 study on ensemble deep learning for emotion recognition from EEG recordings, which has garnered over 170 citations. In this work, he demonstrated that combining Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) networks significantly improves the accuracy of classifying human emotional states from brainwave data—a breakthrough with implications for mental health monitoring and adaptive user interfaces. More recently, Sharma has advanced the field of surface electromyography (sEMG)-based automated grasp recognition, publishing a comprehensive review in 2024 that synthesizes recent progress in prosthetics and virtual reality control. He is also exploring brain-robot interfaces for upper limb motor imagery, contributing to the development of systems that allow users to control robotic devices through thought alone. With a growing citation impact and a focus on translating neural signals into practical robotic control, Sharma’s work is paving the way for more responsive, human-centered assistive technologies.