# EEG to fMRI Synthesis: Is Deep Learning a candidate?

arXiv: https://arxiv.org/abs/2009.14133 · HTML: https://mlsanity.com/paper/2009.14133/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2020.

## Built on by (most cited first, 8 of 8)

- [CATD: Unified Representation Learning for EEG-to-fMRI Cross-Modal Generation](https://mlsanity.com/paper/2408.00777/) (2024)
- [NeuroBOLT: Resting-state EEG-to-fMRI Synthesis with Multi-dimensional Feature Mapping](https://mlsanity.com/paper/2410.05341/) (2024)
- [Deep Generative Models for Physiological Signals: A Systematic Literature Review](https://mlsanity.com/paper/2307.06162/) (2023)
- [NT-ViT: Neural Transcoding Vision Transformers for EEG-to-fMRI Synthesis](https://mlsanity.com/paper/2409.11836/) (2024)
- [From Brainwaves to Brain Scans: A Robust Neural Network for EEG-to-fMRI Synthesis](https://mlsanity.com/paper/2502.08025/) (2025)
- [Latent Representation Learning for Multimodal Brain Activity Translation](https://mlsanity.com/paper/2409.18462/) (2024)
- [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)
- [MEL: Coordinate-Preserving EEG Tokenization for fMRI Translation](https://mlsanity.com/paper/2608.29304/) (2026)

## Abstract (arXiv)

Advances on signal, image and video generation underly major breakthroughs on generative medical imaging tasks, including Brain Image Synthesis. Still, the extent to which functional Magnetic Ressonance Imaging (fMRI) can be mapped from the brain electrophysiology remains largely unexplored. This work provides the first comprehensive view on how to use state-of-the-art principles from Neural Processing to synthesize fMRI data from electroencephalographic (EEG) data. Given the distinct spatiotemporal nature of haemodynamic and electrophysiological signals, this problem is formulated as the task of learning a mapping function between multivariate time series with highly dissimilar structures. A comparison of state-of-the-art synthesis approaches, including Autoencoders, Generative Adversarial Networks and Pairwise Learning, is undertaken. Results highlight the feasibility of EEG to fMRI brain image mappings, pinpointing the role of current advances in Machine Learning and showing the relevance of upcoming contributions to further improve performance. EEG to fMRI synthesis offers a way to enhance and augment brain image data, and guarantee access to more affordable, portable and long-lasting protocols of brain activity monitoring. The code used in this manuscript is available in Github and the datasets are open source.