NeuroWarn BCI: Enhancing Safety in EEG-Controlled Wheelchairs with an RNN-Based Warning System
NeuroWarn BCI is a safety enhancement system for EEG-controlled wheelchairs that uses a Recurrent Neural Network (RNN) to predict potential hazards and provide warnings to users. The system integrates brain-computer interface technology with obstacle detection sensors to create a safer mobility experience for users with severe motor disabilities.
- Alecxander Jamille Andaya
- Kyle E. Billones
- Matthew Ariel A. Enarle
- Jasper M. Nillos
- Shayne B. Yanson
College of Information and Communications Technology
West Visayas State University
La Paz, Iloilo City, Philippines
- 🖥️ /src - Source code for the entire system
- 🔌 /src/backend - Python code for BCI and hardware control
- 🌐 /src/frontend - Web dashboard interface
- 📝 /docs - Documentation and user guides
- 🔧 /hardware - Hardware schematics and configurations
- 🤖 /models - Trained RNN models
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Clone this repository
git clone https://github.com/yourusername/neurowarn.git cd neurowarn -
Follow component-specific guides:
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See the main source README for system overview
- 📋 User Manual - Complete usage guide
- 📄 Thesis Manuscript - Academic paper
Special thanks to Mark Solidarios for his guidance and mentorship throughout the development of this project. We would also like to thank the faculty of the College of Information and Communications Technology at West Visayas State University for their support and valuable feedback.
2025 All Rights Reserved
This project is the intellectual property of the authors and the College of Information and Communications Technology, West Visayas State University. See the DISCLAIMER.md file for details.
This project is intended for academic and research purposes only.