From Brainwaves to Vision: EEG-to-Image Generation

Overview

This project focuses on reconstructing images from EEG (Electroencephalography) signals using Stable Diffusion, aimed at helping locked-in patients communicate through brain signals.

Problem Statement

Locked-in patients have limited means of communication. By decoding brain signals, we can potentially help them express their thoughts and visualize what they’re thinking about.

Technical Approach

The project addresses the challenge of noisy EEG data through innovative encoding techniques combined with the power of Stable Diffusion for image generation.

Key Technologies

• PyTorch: Deep learning framework for model implementation
• Stable Diffusion: State-of-the-art generative model for image synthesis
• Generative AI: Advanced AI techniques for creating realistic images
• Computer Vision: Processing and understanding visual information

Challenges

• Dealing with noisy EEG signals
• Mapping brain signals to visual representations
• Ensuring accurate reconstruction of intended images

Results

The project demonstrates promising results in translating EEG signals into visual representations, opening new possibilities for assistive communication technologies.