EEG-Based Motor Imagery BCI for Robotic Arm Control
The SIPPRE Research Group (Signal, Image Processing and Pattern Recognition Group) is pleased to share a progress update from the ongoing diploma thesis of Panagiotis Leventogiannis, entitled:
“EEG-Based Motor Imagery Brain–Computer Interface (BCI) for Robotic Arm Control.”
Current Progress
As demonstrated in the latest experimental video, the robotic hand successfully performs controlled finger closing, validating the mechanical design and actuation pipeline of the system.
The robotic arm and hand are fully 3D-printed in the SIPPRE Lab using the Creality K1C printer, featuring a tendon-driven mechanism optimized for smooth and precise motion.
Research Scope
The thesis aims to develop an end-to-end BCI system that translates EEG-based motor imagery into real-time control commands for a robotic arm. The work brings together:
- Robotics & mechatronics (3D-printed arm/hand, actuation)
- Brain–Computer Interfaces (BCI)
- EEG signal acquisition and processing
- Machine learning for motor imagery decoding
What’s Next
The next phase of the project will focus on:
- EEG recordings from human subjects
- Training and evaluation of motor imagery classifiers
- Closing the loop between brain signals and robotic control
📣 Call for Volunteers Coming Soon
Announcements for EEG recording sessions and volunteer participation will follow shortly. Stay tuned to the SIPPRE website and social channels for updates.
This project reflects SIPPRE’s ongoing research efforts in BCI systems, neuroengineering, and human–machine interaction, bridging brain signal analysis with real-world robotic applications.