# 🧠 Multimodal Brain Encoder

A **real** brain encoding model that predicts fMRI brain activity from multimodal inputs (images, text, audio).

## Architecture

| Component | Details |
|-----------|---------|
| Feature Extractor | CLIP ViT-L/14 (openai/clip-vit-large-patch14) |
| Feature Layers | Layers 6, 12, 18, 24 CLS tokens concatenated (4096-dim) |
| Brain Encoder | Deep network: 4096 → 2048 → 2048 → 1024 → N_voxels |
| ROI Heads | 5 functional network-specific attention heads |
| Ridge Baseline | sklearn RidgeCV (Algonauts 2023 recipe) |
| Q&A System | Grounded LLM interpreter (Qwen2.5-72B) |

## Training Data

- **Dataset**: [Natural Scenes Dataset (NSD)](https://huggingface.co/datasets/pscotti/naturalscenesdataset)
- **Subject**: subj01 (7T fMRI)
- **Training samples**: 2000 images with paired fMRI responses
- **Validation**: 200 images
- **Voxels**: ~47,236 (nsdgeneral mask)

## Brain Regions (24 ROIs)

| Network | Regions | Function |
|---------|---------|----------|
| Early Visual | V1v, V1d, V2v, V2d, V3v, V3d, hV4 | Basic visual processing |
| Body Selective | EBA, FBA-1, FBA-2, mTL-bodies | Body/person perception |
| Face Selective | OFA, FFA-1, FFA-2, mTL-faces, aTL-faces | Face recognition |
| Place Selective | OPA, PPA, RSC | Scene/navigation |
| Word Selective | OWFA, VWFA-1, VWFA-2, mfs-words, mTL-words | Reading/text |

## How It Works

1. **Input** → CLIP ViT-L/14 multi-layer features (4096-dim)
2. **Brain Encoder** → Predicted fMRI voxel activations (~47k voxels)
3. **ROI Analysis** → Per-region activation summaries with uncertainty
4. **LLM Q&A** → Grounded interpretation (only references model outputs)

## References

- Allen et al. (2022). A massive 7T fMRI dataset. *Nature Neuroscience*
- Gifford et al. (2023). The Algonauts Project 2023 Challenge
- Radford et al. (2021). Learning Transferable Visual Models (CLIP)
- Adeli & Zelinsky (2025). Transformer Brain Encoders (arxiv:2505.17329)

## Usage

```python
from huggingface_hub import hf_hub_download
import torch, numpy as np

# Load model
model_path = hf_hub_download(repo_id="ryu34/multimodal-brain-encoder", filename="best_model.pt")
checkpoint = torch.load(model_path, map_location="cpu", weights_only=False)

# Load your CLIP features (4096-dim multi-layer)
# features = extract_clip_features(image)  # See app.py for full pipeline

# Predict brain activity
model = BrainEncoder(**checkpoint['config'])
model.load_state_dict(checkpoint['model_state_dict'])
predictions = model(features)  # [1, n_voxels]
```