README.md

---
license: apache-2.0
language:
- en
tags:
- fMRI
- EEG
- neuroscience
- brain-imaging
- science
- huggingscience
size_categories:
- 100K<n<1M
---

# 📚 Dataset Card for CineBrain

[![ArXiv](https://img.shields.io/badge/ArXiv-2503.06940-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/2503.06940)

**CineBrain** is a **large-scale multimodal brain dataset** comprising **fMRI, EEG, and ECG** recordings collected while participants watched episodes of The Big Bang Theory.
It supports research on **neural decoding, multimodal learning, and modality transfer** in naturalistic narrative processing. 

---

## 🧠 Dataset Description
### Summary
- **Participants**: 6 subjects  
- **Stimuli**: 30 episodes of *The Big Bang Theory* (first 18 minutes per episode)  
- **Recording time**: 6 hours per subject (36 hours total)  
- **Modalities**:
  - fMRI: TR = 0.8s
  - EEG: 64 channels, 1000 Hz
  - ECG: synchronous recording  

### Supported Tasks
- **Multimodal Brain Analysis**: Investigating relationships between audiovisual stimuli and neural responses  
- **Neural Decoding**: Inferring cognitive states from fMRI and EEG signals  
- **Cross-Modal Learning**: Learning shared representations across fMRI, EEG, and ECG  
- **Modality Transfer**: Predicting **fMRI from EEG** and **EEG from fMRI** 
---

## 📂 Dataset Structure
### Repository Contents
- `videos.tar`: Video stimuli (8100 clips from 30 episodes)  
- `sub-00xx/`: Participant folders with raw + preprocessed fMRI/EEG  
- `captions-qwen-2.5-vl-7b.json`: Auto-generated video captions  

### Inside Each Participant Folder
- `fMRI_raw_data.tar` – raw fMRI  
- `fMRI_preprocessed_data.tar` – preprocessed fMRI  
- `EEG_preprocessed_data.tar` – preprocessed EEG  

### Data Statistics
| Modality | Sampling | Duration | Size (approx.) |
|----------|----------|----------|----------------|
| fMRI     | TR=0.8s  | 6h/subject | ~12 GB total |
| EEG      | 1000 Hz, 64 ch | 6h/subject | ~72 GB total |
| Video    | 30 eps × 18 min | 8100 clips | ~2.59 GB |

### Data Splits
- **Subjects 1, 2, 6**: Episodes 1–20 (5400 clips)  
- **Subjects 3, 4, 5**: Episodes 1–10 and 21–30 (5400 clips)  

Total: 36 hours of brain recordings across all subjects

---

## 📌 Important Notes
- **Data Release**: Fully open and downloadable.
- **Cross-Dataset Correspondence**: Subjects **1, 2, 3, and 4** in CineBrain correspond to Subjects **6, 8, 1, and 4** in the [fMRI-Shape](https://huggingface.co/datasets/Fudan-fMRI/fMRI-Shape) and [fMRI-Objaverse](https://huggingface.co/datasets/Fudan-fMRI/fMRI-Objaverse) datasets.
---

## 🏗 Dataset Creation
### Motivation
CineBrain was designed to support **naturalistic neuroscience research**, focusing on:  
- Narrative comprehension  
- Multisensory integration  
- Individual variability in brain responses  
- Temporal dynamics of engagement  

### Source Data & Preprocessing
- **fMRI**: High temporal resolution (TR=0.8s)  
- **EEG**: High sampling rate (1000 Hz)  
- **Preprocessing**: Standard pipelines, artifact removal, quality control  

⚠️ **Ethics**: All data anonymized. Please follow ethical guidelines when using human neuroimaging data.  

---


## ⚖️ Considerations for Using the Data
### Social Impact
This dataset may advance:  
- Understanding of **complex narrative processing**  
- **Brain-computer interfaces**  
- **Clinical applications** for attention & comprehension disorders  

### Potential Biases
- **Demographic bias**: Limited participant diversity  
- **Cultural bias**: English-language sitcom  
- **Selection bias**: Likely university volunteers  

---

## 📎 Additional Information
- **License**: [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)  
- **Languages**: English audiovisual content

## Citation Information
If you find our paper useful for your research and applications, please cite using this BibTeX:

```bibtex
@misc{gao2025cinebrain,
      title={CineBrain: A Large-Scale Multi-Modal Brain Dataset During Naturalistic Audiovisual Narrative Processing}, 
      author={Jianxiong Gao and Yichang Liu and Baofeng Yang and Jianfeng Feng and Yanwei Fu},
      year={2025},
      eprint={2503.06940},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2503.06940}, 
}
```