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Automated detection and labelling of high-density EEG electrodes from structural MR images

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Author Marino Marco , Quanyiung Li , Brem Silvia , Wenderoth Nicole , Mantini Dante ,
Project Neuronal markers of grapheme-phoneme training response for prediction of successful reading acquisition in children at familial risk for developmental dyslexia
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Original article (peer-reviewed)

Journal Journal of Neural Engineering
Volume (Issue) 13
Page(s) 1 - 11
Title of proceedings Journal of Neural Engineering
DOI 10.1088/1741-2560/13/5/056003

Open Access

URL https://doi.org/10.1088/1741-2560/13/5/056003
Type of Open Access Publisher (Gold Open Access)

Abstract

Objective - Accurate knowledge about the positions of electrodes in electroencephalography (EEG) is very important for precise source localizations. Direct detection of electrodes from magnetic resonance (MR) images is particularly interesting, as it is possible to avoid errors of co-registration between electrode and head coordinate systems. In this study, we propose an automated MR-based method for electrode detection and labeling, particularly tailored to high-density montages. Approach - Anatomical MR images were processed to create an electrode-enhanced image in individual space. Image processing included intensity non-uniformity correction, background noise and goggles artifact removal. Next, we defined a search volume around the head where electrode positions were detected. Electrodes were identified as local maxima in the search volume and registered to the MNI standard space using an affine transformation. This allowed the matching of the detected points with the specific EEG montage template, as well as their labeling. Matching and labeling were performed by the Coherent Point Drift method. Our method was assessed on 8 MR images collected in subjects wearing a 256-channel EEG net, using the displacement with respect to manually selected electrodes as performance metric. Main results - Average displacement achieved by our method was significantly lower compared to alternative techniques, such as the photogrammetry technique. The maximum displacement was for more than 99% of the electrodes lower than 1 cm, which is typically considered an acceptable upper limit for errors in electrode positioning. Our method showed robustness and reliability, even in suboptimal conditions, such as in the case of net rotation, imprecisely gathered wires, electrode detachment from the head, and MR image ghosting. Significance - We showed that our method provides objective, repeatable and precise estimates of EEG electrode coordinates. We hope our work will contribute to a more widespread use of high-density EEG as a brain-imaging tool.
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