E2fNet - Official Implementation

From Brainwaves to Brain Scans: A Robust Neural Network for EEG-to-fMRI Synthesis

Authors: Kristofer Grover Roos, Atsushi Fukuda, Quan Huu Cap
Paper: https://arxiv.org/abs/2502.08025

Abstract While functional magnetic resonance imaging (fMRI) offers rich spatial resolution, it is limited by high operational costs and significant infrastructural demands. In contrast, electroencephalography (EEG) provides millisecond-level precision in capturing electrical activity but lacks the spatial resolution necessary for precise neural localization. To bridge these gaps, we introduce E2fNet, a simple yet effective deep learning model for synthesizing fMRI images from low-cost EEG data. E2fNet is specifically designed to capture and translate meaningful features from EEG across electrode channels into accurate fMRI representations. Extensive evaluations across three datasets demonstrate that E2fNet consistently outperforms existing methods, achieving state-of-the-art results in terms of the structural similarity index measure (SSIM). Our findings suggest that E2fNet is a promising, cost-effective solution for enhancing neuroimaging capabilities.

Required env

• CUDA >= 11
• Python >= 3.10
• Poetry >= 2.0

Installation

poetry install --no-root

Datasets pre-processing

See datasets pre-processing for download and pre-process all datasets

Training

Modify run_bash.sh and run

bash run_train.sh

Note: The current code loads ALL training data to RAM, if you don't have enough RAM, please modify the eeg2fmri_datasets.py to load smaller chunks of data

Pre-trained models

TBD ...

Inference

See inference.ipynb for more details.

Citation

@article{kristofer2025e2fnet,
  title   = {From Brainwaves to Brain Scans: A Robust Neural Network for EEG-to-fMRI Synthesis},
  author  = {Kristofer Grover Roos and Atsushi Fukuda and Quan Huu Cap},
  journal = {arXiv preprint},
  year    = {2025}
}