A Real-Time BCI for Stroke Hand Rehabilitation Using Latent EEG Features from Healthy Subjects
Abstract
This study presents a real-time, portable brain-computer interface (BCI) system designed to support hand rehabilitation for stroke patients. The system combines a low cost 3D-printed robotic exoskeleton with an embedded controller that converts brain signals into physical hand movements. EEG signals are recorded using a 14-channel Emotiv EPOC+ headset and processed through a supervised convolutional autoencoder (CAE) to extract meaningful latent features from single-trial data. The model is trained on publicly available EEG data from healthy individuals (WAY-EEG-GAL dataset), with electrode mapping adapted to match the Emotiv headset layout. Among several tested classifiers, Ada Boost achieved the highest accuracy (89.3%) and F1-score (0.89) in offline evaluations. The system was also tested in real time on five healthy subjects, achieving classification accuracies between 60% and 86%. The complete pipeline - EEG acquisition, signal processing, classification, and robotic control - is deployed on an NVIDIA Jetson Nano platform with a real-time graphical interface. These results demonstrate the system's potential as a low-cost, standalone solution for home-based neurorehabilitation.
Cite
@article{arxiv.2510.15890,
title = {A Real-Time BCI for Stroke Hand Rehabilitation Using Latent EEG Features from Healthy Subjects},
author = {F. M. Omar and A. M. Omar and K. H. Eyada and M. Rabie and M. A. Kamel and A. M. Azab},
journal= {arXiv preprint arXiv:2510.15890},
year = {2025}
}
Comments
Proceedings of the 7th Novel Intelligent and Leading Emerging Sciences Conference (NILES 2025)