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ORIGINAL RESEARCH article

Front. Neurosci.
Sec. Brain Imaging Methods
Volume 18 - 2024 | doi: 10.3389/fnins.2024.1444935

Comprehensive analysis of supervised learning methods for electrical source imaging

Provisionally accepted
Sarah Reynaud Sarah Reynaud 1,2*Adrien Merlini Adrien Merlini 1,3Douraied Ben Salem Douraied Ben Salem 2,4François Rousseau François Rousseau 1,2
  • 1 IMT Atlantique Bretagne-Pays de la Loire, Nantes, France
  • 2 INSERM U1101 Laboratoire de Traitement de l'information Médicale (LaTIM), Brest, Brittany, France
  • 3 UMR6285 Laboratoire des Sciences et Techniques de l'Information, de la Communication et de la Connaissance (LAB-STICC), Brest, Brittany, France
  • 4 Centre Hospitalier Regional Universitaire (CHU) de Brest, Brest, Brittany, France

The final, formatted version of the article will be published soon.

    Electroencephalography source imaging (ESI) is an ill-posed inverse problem: an additional constraint is needed to find a unique solution. The choice of this constraint, or prior, remains a challenge for most ESI methods. This work explores the application of supervised learning methods for spatio-temporal ESI, where the relationship between measurements and sources is learned directly from the data. Three neural networks were trained on synthetic data and compared with non-learning based methods. Two distinct types of simulation, each based on different models of brain electrical activity, were employed to quantitatively assess the generalisation capabilities of the neural networks and the impact of training data on their performances, using five complementary metrics. The results demonstrate that, with appropriately designed simulations, neural networks can be competitive with non-learning-based approaches, even when applied to previously unseen data.

    Keywords: Electroencepahlography, deep learning, inverse problem, Neuroimaging, Data simulation

    Received: 06 Jun 2024; Accepted: 18 Oct 2024.

    Copyright: © 2024 Reynaud, Merlini, Ben Salem and Rousseau. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Sarah Reynaud, IMT Atlantique Bretagne-Pays de la Loire, Nantes, France

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.