# From Brainwaves to Brain Scans: A Robust Neural Network for EEG-to-fMRI Synthesis Source: [From Brainwaves to Brain Scans: A Robust Neural Network for EEG-to-fMRI Synthesis]() Collection index: [llms.txt]() Data updated: 2025-10-26T04:35:18.340Z Authors: Kristofer Grover Roos, Atsushi Fukuda, Quan Huu Cap Published: 2025-02-11T23:55:16.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [fMRI Reconstruction on CineBrain Whole Brain](): fMRI Reconstruction; CineBrain Whole Brain; MSE: 0.188. - [fMRI Reconstruction on CineBrain Visual Cortex](): fMRI Reconstruction; CineBrain Visual Cortex; MSE: 0.196. ## Original sources and implementation [Original paper]() [PDF]()