Skip to Main content Skip to Navigation
New interface
Journal articles

Deep learning-based localization of EEG electrodes within MRI acquisitions

Caroline Pinte 1 Mathis Fleury 1 Pierre Maurel 1 
1 EMPENN - Neuroimagerie: méthodes et applications
INSERM - Institut National de la Santé et de la Recherche Médicale, Inria Rennes – Bretagne Atlantique , IRISA-D6 - SIGNAL, IMAGE ET LANGAGE
Abstract : The simultaneous acquisition of electroencephalographic (EEG) signals and functional magnetic resonance images (fMRI) aims to measure brain activity with good spatial and temporal resolution. This bimodal neuroimaging can bring complementary and very relevant information in many cases and in particular for epilepsy. Indeed, it has been shown that it can facilitate the localization of epileptic networks. Regarding the EEG, source localization requires the resolution of a complex inverse problem that depends on several parameters, one of the most important of which is the position of the EEG electrodes on the scalp. These positions are often roughly estimated using fiducial points. In simultaneous EEG-fMRI acquisitions, specific MRI sequences can provide valuable spatial information. In this work, we propose a new fully automatic method based on neural networks to segment an ultra-short echo-time MR volume in order to retrieve the coordinates and labels of the EEG electrodes. It consists of two steps: a segmentation of the images by a neural network, followed by the registration of an EEG template on the obtained detections. We trained the neural network using 37 MR volumes and then we tested our method on 23 new volumes. The results show an average detection accuracy of 99.7% with an average position error of 2.24 mm, as well as 100% accuracy in the labeling.
Document type :
Journal articles
Complete list of metadata
Contributor : Agnès Hermann Connect in order to contact the contributor
Submitted on : Tuesday, June 28, 2022 - 10:52:58 AM
Last modification on : Friday, September 16, 2022 - 9:08:05 AM


Publication funded by an institution



Caroline Pinte, Mathis Fleury, Pierre Maurel. Deep learning-based localization of EEG electrodes within MRI acquisitions. Frontiers in Neurology, 2021, ⟨10.3389/fneur.2021.644278⟩. ⟨inserm-03214269v3⟩



Record views


Files downloads