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.
Highlights
Functional magnetic resonance imaging is a technique that allows to visualize brain activity by detecting hemodynamic variations
Recent studies have shown the contribution that simultaneous EEG-Functional magnetic resonance imaging (fMRI) can make to the understanding and treatment of epilepsy, for example in identifying epileptogenic networks [3,4,5]
The training lasts between 1 and 2 weeks, depending on the number of processes launched on the GPU available
Summary
Functional magnetic resonance imaging (fMRI) is a technique that allows to visualize brain activity by detecting hemodynamic variations It is a non-invasive method that is widely used for the study of brain function [see for example [1]]. Electroencephalography (EEG) is a technique for measuring the electrical activity of the brain by using electrodes placed on the scalp, which is a non-invasive method, widely used for the diagnosis of brain disorders and the study of neurophysiological activity [2]. Position errors lead to inaccuracies in the estimation of the EEG inverse solution [9] This is an even more important issue in the case of studies involving simultaneous EEG/fMRI acquisitions, where several sessions and several EEG cap installations can be required. It is essential to be able to obtain the EEG electrode positions reliably and accurately
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