Semi-Automated and Direct Localization and Labeling of EEG Electrodes Using MR Structural Images for Simultaneous fMRI-EEG

Abstract

Electroencephalography (EEG) source reconstruction estimates spatial information from the brain’s electrical activity acquired using EEG. This method requires accurate identification of the EEG electrodes in a three-dimensional (3D) space and involves spatial localization and labeling of EEG electrodes. Here, we propose a new approach to tackle this two-step problem based on the simultaneous acquisition of EEG and magnetic resonance imaging (MRI). For the step of spatial localization of electrodes, we extract the electrode coordinates from the curvature of the protrusions formed in the high-resolution T1-weighted brain scans. In the next step, we assign labels to each electrode based on the distinguishing feature of the electrode’s distance profile in relation to other electrodes. We then compare the subject’s electrode data with template-based models of prelabeled distance profiles of correctly labeled subjects. Based on this approach, we could localize EEG electrodes in 26 head models with over 90% accuracy in the 3D localization of electrodes. Next, we performed electrode labeling of the subjects’ data with progressive improvements in accuracy: with ∼58% accuracy based on a single EEG-template, with ∼71% accuracy based on 3 EEG-templates, and with ∼76% accuracy using 5 EEG-templates. The proposed semi-automated method provides a simple alternative for the rapid localization and labeling of electrodes without the requirement of any additional equipment than what is already used in an EEG-fMRI setup.

Variability of EEG electrode positions and their underlying brain regions: visualizing gel artifacts from a simultaneous EEG‐fMRI dataset

“…We took advantage of a pre‐existing neuroimaging dataset taken from a combined EEG and functional magnetic resonance imaging (fMRI) experiment, using 64 channel fixed electrode caps with a 10‐10 electrode layout (Scrivener et al., 2021 ). Whilst several groups have developed methods to recover EEG electrode positions from simultaneous EEG‐fMRI data using specific MRI acquisition methods (Butler et al., 2017 ) or reconstruction from acquired structural scans (Bhutada et al., 2020 ; Brinkmann et al., 1998 ; de Munck et al., 2012 ; Jurcak et al., 2005 ; Koessler et al., 2008 ; Kozinska et al., 2001 ; Lamm et al., 2001 ; Marino et al., 2016 ; Silva et al., 2016 ; Whalen et al., 2008 ), these approaches often require methods and toolboxes that are not yet widely used. As such, we additionally highlight a novel and simple way of projecting electrode locations to the cortical surface using electrode gel artifacts (that appear on the MR image underlying electrode positions) and commercially available equipment.…”

Systematic Review Between Resting-State fMRI and Task fMRI in Planning for Brain Tumour Surgery

“… 29 Therefore, the behavioral protocol is rather straightforward (“keep waiting and try to remain awake”), and no extra task-related devices, such as MR-compatible display systems, are needed. 30 , 31 Additionally, Rs-fMRI is compatible with light or sleep sedation and may be performed on young children; consequently, the conclusions are more difficult. 32 , 33 Therefore, this study intends to comprehensively examine the relevance of fMRI in the neurosurgical planning of brain tumor resections, specifically focusing on the shift from task-based to resting-state modalities.…”