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.
Highlights
Despite the usefulness of electroencephalography (EEG) to study the dynamic changes in brain signal, one of its historical weaknesses has been its restricted spatial resolution
Localization and labeling of EEG electrodes are critical for the analysis of EEG data, especially for source reconstruction
Our method does not rely on special MR acquisition sequences (Butler et al, 2017; Fleury et al, 2019), which may not be available in standard research or clinical setups
Summary
Despite the usefulness of electroencephalography (EEG) to study the dynamic changes in brain signal, one of its historical weaknesses has been its restricted spatial resolution. To label the electrodes on a test subject, a minimization algorithm is applied on the coordinates of the subject’s head and the previously prepared template head in order to automatically provide labels for each of the electrodes These methods are closer to fully automatic approaches to solving the two-step problem; a limitation to these techniques is that they both require manual selection of points to localize the electrodes which can be quite time-consuming. Marino et al (2016) reported an automatic method devised for high-density electrode caps, which extracts the electrode position through image processing and labels the electrodes using a transformation of the candidate position to MNI space to be matched with a template of the desired EEG positions Another method (Fleury et al, 2019) allows the localization of electrodes without relying on the presence of conductive gel ( it uses additional UTE sequences, mentioned above) and implements automatic labeling using the iterative Closest Point algorithm, over a template of the electrode cap. In this way we present an approach that provides a simple solution to the problem of localizing and labeling electrodes
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