Semi-Automated and Direct Localization and Labeling of EEG Electrodes Using MR Structural Images for Simultaneous fMRI-EEG - Bhutada, Abhishek S.; Sepúlveda, Pradyumna; Torres, Rafael; Ossandón, Tomás; Ruiz, Sergio; Sitaram, Ranganatha (Frontiers in Neuroscience, 2020)
EEG source reconstruction estimates spatial information from the brain’s electrical activity acquired using Electroencephalography (EEG). This method requires accurate identification of the EEG electrodes in a 3-dimensional 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 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.