EEG-Based Identification System

This project leverages EEG (electroencephalography) signals for biometric authentication, focusing on a Brain-Computer Interface (BCI) application. By utilizing the Steady-State Visual Evoked Potential (SSVEP) technique, we aim to deliver a robust and effective authentication method.

Overview

Traditional biometrics, such as facial recognition and fingerprinting, have known vulnerabilities. EEG-based identification offers a promising alternative, measuring unique brain responses to specific visual stimuli.

Key Advantages

• Security and Resilience: EEG signals are inherently secure, difficult to replicate, and resistant to spoofing.
• Convenience: Users can be authenticated without traditional methods like passwords or fingerprints.
• Universal and Unique: EEG collection is universally applicable and distinct for each individual.

Current Challenges

• Signal Stability: Ensuring consistent and interference-free EEG signal captures is crucial.
• Environmental Adaptability: Algorithms must perform reliably across different collection environments.

Project Contributions

1. BCI Application Design: Developed an EEG authentication application using SSVEP technique, achieving a 100% accuracy rate with a Random Forest model.

2. Experimental Setup: Conducted EEG experiments using a visual interface to simulate and gather real-world data.

3. Model Analysis and Enhancement: Focused on EEG model decomposition and analysis to ensure the most distinguishable patterns are used for reliable user identification.

4. Advanced Machine Learning: Implemented Python-based classification methods to process and classify EEG data, enhancing system robustness and adaptability.

Future Applications

Our method can be used in real-world scenarios, such as unlocking personal spaces (e.g., emails) through a visual interface, providing enhanced user convenience and security.

Getting Started

To explore or contribute to this project:

1. Develop Visual Interface: Use Python for creating visual stimuli interfaces.
2. Collect EEG Data: Utilize an EEG headset for data acquisition.
3. Process and Classify Data: Apply machine learning techniques to analyze data, ensuring reliable and accurate authentication.

By exploring EEG potentials, this project pioneers secure and convenient biometric authentication. Your feedback and collaboration are highly valued.

Contact

For inquiries or collaboration opportunities, please reach out to our development team.