Reinmar J. Kobler
Papers
5
Total Citations
153
H-Index
5
About
Reinmar J. Kobler is a leading researcher in non-invasive brain-computer interfaces (BCIs), with a primary focus on restoring motor function for individuals with paralysis. His work centers on decoding continuous arm and hand movement intentions from low-frequency EEG signals, aiming to create intuitive, closed-loop control of robotic arms and neuroprostheses. Kobler’s major contributions include demonstrating the feasibility of continuous, real-time decoding of arm movement trajectories from non-invasive EEG, a significant step beyond offline analyses. His 2020 paper on this topic has garnered 87 citations, underscoring its impact. He has also pioneered frameworks that integrate error processing and goal-directed movement detection to gate kinesthetic feedback, enhancing the naturalness of prosthetic control—work supported by an ERC-funded project, “Feel Your Reach.” Additionally, Kobler has applied machine learning to reveal population vector-like representations from EEG, bridging invasive and non-invasive decoding approaches. His research directly targets users with spinal cord injury, aiming to translate these advances into practical, life-changing assistive technologies. Through his innovative methodology and translational focus, Kobler is shaping the future of non-invasive neural control for neuroprosthetics.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3Using machine learning to reveal the population vector from EEG signals20 citations · 2020
- 4
- 5