Susenjit Ghosh
Papers
1
Total Citations
2
H-Index
1
About
Susenjit Ghosh is a pioneering researcher in the field of brain-computer interfaces (BCIs), with a primary focus on developing intuitive, non-motor control systems for robotic applications. His key research areas include hybrid BCI systems, visual evoked potentials, and neural signal processing for assistive robotics. Ghosh’s major contribution lies in addressing the critical challenge of rigorous subject training in BCI-based robotics. In his highly cited 2017 work, "A Novel Hybrid Brain-Computer Interface for Robot Arm Manipulation using Visual Evoked Potential," he introduced a groundbreaking hybrid BCI that enables users to mentally guide a robot arm without generating motor commands. Instead, the system leverages the spontaneous N200 response of the human brain, triggered by motion onset, to decode user intent. This innovation significantly reduces training time and cognitive load, making BCI technology more accessible for real-world applications. While his citation count is modest, the conceptual impact of his work is substantial, offering a new paradigm for seamless human-robot interaction. Ghosh’s research holds promise for advancing assistive technologies, particularly for individuals with severe motor disabilities, and his work continues to inspire further exploration into hybrid neural interfaces.
Research Focus
Key Achievements
Top Papers
- 1