Papers
7
Total Citations
38
H-Index
3
About
Lidia Ghosh is a pioneering researcher at the intersection of brain-computer interfaces (BCI), robotics, and rehabilitation engineering. Her work focuses on decoding electroencephalography (EEG) signals to enable intuitive, real-time control of robotic systems—from manipulator arms and mobile robots to assistive wheelchairs. Ghosh’s major contributions include developing hybrid BCI paradigms that integrate attention-based deep learning models, such as GRU networks, with event-driven nonlinear model predictive control (NMPC) to enhance tracking performance even under actuator failures. She has also introduced innovative frameworks for EEG-induced error correction in path planning using learning automata and for autonomous game-teaching, where a robot arm learns from human trainers via reinforcement learning. With over 38 citations across her most-cited works, Ghosh’s research has direct implications for assistive technology, motor rehabilitation, and human-robot collaboration. Her notable achievement includes a 2021 study on EEG-induced game-teaching, which demonstrates how a robot can learn gaming actions from an experienced player to train younger children—a creative application of BCI in education and therapy. Ghosh’s work is driving the next generation of adaptive, brain-controlled robotic systems that promise to restore mobility and independence for individuals with motor impairments.
Research Focus
Key Achievements
Top Papers
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- 3Hybrid brain-computer interfacing paradigm for assistive robotics3 citations · 2024
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