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

arXiv: https://arxiv.org/abs/2502.08025 · HTML: https://mlsanity.com/paper/2502.08025/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 11 Feb 2025.

## Builds on (most cited first, 6 of 6)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [FMRI data augmentation via synthesis](https://mlsanity.com/paper/1907.06134/) (2019)
- [EEG to fMRI Synthesis: Is Deep Learning a candidate?](https://mlsanity.com/paper/2009.14133/) (2020)
- [EEG to fMRI Synthesis Benefits from Attentional Graphs of Electrode Relationships](https://mlsanity.com/paper/2203.03481/) (2022)
- [NT-ViT: Neural Transcoding Vision Transformers for EEG-to-fMRI Synthesis](https://mlsanity.com/paper/2409.11836/) (2024)

## Built on by (most cited first, 4 of 4)

- [Spec2VolCAMU-Net: A Spectrogram-to-Volume Model for EEG-to-fMRI Reconstruction based on Multi-directional Time-Frequency Convolutional Attention Encoder and Vision-Mamba U-Net](https://mlsanity.com/paper/2505.09521/) (2025)
- [EEG-to-fMRI synthesis of task-evoked and spontaneous brain activity: addressing issues of statistical significance and generalizability](https://mlsanity.com/paper/2504.10752/) (2025)
- [Modeling Spatiotemporal Neural Frames for High Resolution Brain Dynamic](https://mlsanity.com/paper/2603.24176/) (2026)
- [MEL: Coordinate-Preserving EEG Tokenization for fMRI Translation](https://mlsanity.com/paper/2608.29304/) (2026)

## Abstract (arXiv)

While functional magnetic resonance imaging (fMRI) offers valuable insights into brain activity, 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 fidelity necessary for precise neural localization. To bridge these gaps, we propose E2fNet, a simple yet effective deep learning model for synthesizing fMRI images from low-cost EEG data. E2fNet is an encoder-decoder network specifically designed to capture and translate meaningful multi-scale features from EEG across electrode channels into accurate fMRI representations. Extensive evaluations across three public datasets demonstrate that E2fNet consistently outperforms existing CNN- and transformer-based methods, achieving state-of-the-art results in terms of the structural similarity index measure (SSIM). These results demonstrate that E2fNet is a promising, cost-effective solution for enhancing neuroimaging capabilities. The code is available at https://github.com/kgr20/E2fNet.