Neural-Controlled Virtual Hand Using Deep Learning

This work presents a deep learning-based Brain-Computer Interface (BCI) system for controlling a virtual hand in real time. The system processes EEG signals using machine learning techniques and maps them into motor commands for hand movement visualization in a simulated environment.

The proposed framework demonstrates the feasibility of neural signal decoding for assistive technologies and neuroprosthetics.

Video demonstration:
https://www.youtube.com/watch?v=VWWJKNxQdKo