---
license: apache-2.0
language:
- en
---
# NeuroBOLT EEG-fMRI ROI Dataset
**Short name:** `neurobolt-rest`  
**Modalities:** EEG (simultaneous with fMRI), fMRI ROI time series  
**Setting:** Resting-state (eyes closed)  
**Subjects / Scans:** 22 participants, 29 fMRI scans (7 participants with two scans)  
<!-- **Contact for extended data (task-condition or higher-res ROIs):** yamin.li@vanderbilt.edu
 -->
 
---

## 🧠 Overview
This dataset provides synchronized resting-state **EEG** and **BOLD fMRI ROI time series** collected **simultaneously** from healthy adults. It is intended for research on **NeuroAI, cross-modal modeling (EEG-fMRI), multimodal fusion, and hemodynamic modeling**.

We release:
- Preprocessed **fMRI ROI time series** (DiFuMo parcellation, n=64).
- Preprocessed and resampled **EEG** time series aligned to fMRI.

*For **task-condition data**, **higher-resolution DiFuMo ROIs** (>64), or any inquiries, please contact **yamin.li@vanderbilt.edu**.*

## 📖 Data Collection Summary
- **Participants:** 22 healthy volunteers.  
- **Sessions:** Two 20-minute resting sessions per participant (eyes closed); final dataset contains **29 scans** after artifact exclusion.  
- **Ethics:** Written informed consent obtained. Procedures approved by the Institutional Review Board (IRB).

### fMRI
- **Scanner:** 3T  
- **Sequence:** Multi-echo gradient-echo EPI  
- **TR:** 2100 ms  
- **Condition:** Rest (eyes closed)

### EEG
- **System:** MR-compatible, 32-channel (10–20), **FCz** reference (BrainAmps MR, Brain Products GmbH)  
- **Sampling rate:** 5 kHz, synchronized to the **scanner’s 10 MHz clock** (facilitates MR gradient artifact reduction)

---

## 🔩 Preprocessing Summary

### fMRI → ROI time series
- **Parcellation:** DiFuMo (n = 64) with **2 additional global signals**  
  - *global signal clean (cleaned whole-brain average signal, with confounds regressed)*  
  - *global signal raw (unprocessed whole-brain average signal)*
- **Confound regression:** Motion regressors removed  
- **Temporal filtering:** Low-pass filter applied at < 0.15 Hz  
- **Normalization:** Demeaned and scaled by the **95th percentile** of the absolute amplitude (per ROI)

In the paper we evaluated **7 representative ROIs** spanning diverse spatial and functional domains:
- **Primary sensory:** *Cuneus*, *Heschl’s gyrus*  
- **High-level cognitive:** *Precuneus anterior*, *Middle frontal gyrus anterior* 
- **Subcortical:** *Putamen*, *Thalamus*  
- **Global:** *global signal clean*

### EEG
- Channels: ECG/EOG/EMG **removed** → remaining **26 scalp channels**  
- Resampling: **5 kHz → 200 Hz** (retains <100 Hz content, improves efficiency)  
- Alignment: synchronized to fMRI acquisition for one-to-one pairing with BOLD time points  
- Model input convention (if used): **16-second EEG windows** preceding each fMRI TR (covers HRF peak and variance)

*Detailed acquisition and artifact-reduction procedures are documented in the [NeuroBOLT paper’s Appendix D](https://openreview.net/pdf?id=y6qhVtFG77).*

---

## ⛳ Model Checkpoints
- Model checkpoints for selected brain regions are available for reproducing our results:  
👉 [**Hugging Face Repository**](https://huggingface.co/ssssssup/NeuroBOLT/tree/main)  
- A step-by-step tutorial can be found here:  
📘 [**Google Colab Notebook**](https://colab.research.google.com/drive/1e7mfxQqth4mcfqlhTypgB5Q0gYPNbT-y?usp=sharing)



## 📚 Dataset Citation

If you use our dataset in your research or publications, please cite the following paper:

```bibtex
@inproceedings{
  li2024neurobolt,
  title={Neuro{BOLT}: Resting-state {EEG}-to-f{MRI} Synthesis with Multi-dimensional Feature Mapping},
  author={Yamin Li and Ange Lou and Ziyuan Xu and Shengchao Zhang and Shiyu Wang and Dario J. Englot and Soheil Kolouri and Daniel Moyer and Roza G Bayrak and Catie Chang},
  booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
  year={2024},
  url={https://openreview.net/forum?id=y6qhVtFG77}
}

---
license: Apache-2.0
---