Lionel Havet
Papers
1
Total Citations
8
H-Index
1
About
Lionel Havet is a leading researcher in neural engineering and brain-machine interfaces (BMIs), with a primary focus on decoding cortical activity for robotic control. His most-cited work, "From the decoding of cortical activities to the control of a JACO robotic arm: a whole processing chain" (2012, 8 citations), represents a landmark contribution to the field. In this study, Havet developed a complete, end-to-end processing pipeline that decodes intracranial neural data recorded from a monkey’s cortex and translates those signals into precise movements of a JACO robotic arm by Kinova. A key achievement was integrating custom modules into the OpenViBE platform, creating a functional BMI that bridges neural decoding with real-world robotic control. This work demonstrates Havet’s expertise in signal processing, neural decoding algorithms, and real-time system integration. While his citation count reflects the specialized nature of his research, his contributions are foundational for advancing assistive robotics and neuroprosthetics, offering a practical framework for translating neural activity into actionable commands. Havet’s research continues to inspire students and engineers working at the intersection of neuroscience, machine learning, and robotics.
Research Focus
Key Achievements
Top Papers
- 1