Visualizing Thought Through Neural Activity
BrainSync reconstructs visual perception from fMRI and EEG data, bridging the gap between brain activity and visual experience.
Decoding the Neural Language of Vision
BrainSync uses advanced machine learning techniques to translate brain activity into visual representations, providing a window into human perception.
fMRI Processing
Process functional MRI data to extract neural patterns associated with visual stimuli.
EEG Signal Analysis
Analyze EEG signals to identify neural correlates of visual perception.
Deep Learning Models
Utilize state-of-the-art deep learning for accurate visual reconstruction.
Real-time Visualization
See the visual reconstruction process in real-time with our interactive interface.
How BrainSync Works
BrainSync employs a multi-stage process to reconstruct visual stimuli from neural data:
- 1
Data Acquisition
Collect high-quality fMRI or EEG data during visual stimulation.
- 2
Preprocessing
Clean and normalize neural signals to remove noise and artifacts.
- 3
Feature Extraction
Identify patterns in neural activity that correlate with visual features.
- 4
Image Reconstruction
Generate visual output using deep generative models trained on neural data.
Try the Demo
See how BrainSync translates neural activity into visual representations. Upload your own data or try our example files.
Select a sample fMRI file
Neural Activity
No fMRI Data
Upload a fMRI file to see the visualization
Generated Image
Image Generator
Upload your neural data to generate a visualization
Our Research
Bridging neuroscience and computer vision to decode visual perception from brain activity.
Methodology & Findings
Our research combines advanced functional neuroimaging with deep learning techniques to reconstruct visual stimuli from neural signals.
Data Collection
Participants were presented with visual stimuli while undergoing fMRI scanning and EEG recording, creating paired datasets of brain activity and visual input.
Model Architecture
We developed a novel convolutional neural network architecture that learns mappings between neural activity patterns and visual features at multiple scales.
Results
Our model achieved a 78% reconstruction accuracy for basic visual elements and 62% for complex scenes, significantly outperforming previous approaches.
Publications
Neural Decoding of Visual Imagery During Sleep
Journal of Cognitive Neuroscience, 2024
Cross-modal Alignment for EEG-based Visual Reconstruction
Advances in Neural Information Processing Systems, 2023
Real-time fMRI Neurofeedback for Visual Perception
Nature Neuroscience, 2023
Current Research
We're currently exploring how attention modulates visual cortex activity and affects reconstruction quality.