Motor Imagery Bionic Hand
A student team in Saudi Arabia builds an EEG BCI to control a prosthetic hand.
The Build
Their system combined EEG signal acquisition, an NVIDIA Jetson Nano, machine learning, and a prosthetic hand. After early challenges with noisy data and headset design, they used a NeuroPawn EEG headset to improve signal capture and continue developing their BCI pipeline.
Results
The team streamed and stored EEG data, collected samples from multiple participants, and trained an ATCNet-based model to detect movement intention from brain activity. After extensive testing, the system successfully translated the user’s intention to move into prosthetic hand movement.
This project highlights what becomes possible when students have access to practical, affordable neurotechnology tools. At NeuroPawn, we are proud to support hands-on BCI projects that help students move beyond theory and build real-world neuroscience and engineering applications.
