Papers
4
Total Citations
23
H-Index
3
About
Laurent Bougrain is a leading researcher in Brain-Computer Interfaces (BCIs) and neural decoding, with a focus on translating brain activity into real-world robotic control. His work centers on developing complete processing chains that allow users to operate robotic arms—such as the JACO arm by Kinova—directly from cortical signals. Bougrain’s major contributions include pioneering the use of combined motor imageries to expand the command set for BCI-driven prosthetics, enabling more nuanced control beyond simple right-hand or left-hand commands. He has also integrated inverse reinforcement learning into BCI systems, allowing robotic arms to learn optimal behaviors from user intent, thereby enhancing autonomy and efficiency. His research, published in venues like the *International Journal of Human-Computer Studies*, has garnered citations across key papers, including his 2012 work on decoding cortical activities (8 citations) and his 2017 study on combined motor imageries (7 citations). By bridging neural signal processing, machine learning, and robotics, Bougrain’s work advances assistive technologies for individuals with motor impairments, offering a pathway toward seamless human-machine interaction in real-world contexts.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4