Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/33847
Appears in Collections:Psychology Journal Articles
Peer Review Status: Refereed
Title: Variability of EEG electrode positions and their underlying brain regions: visualizing gel artifacts from a simultaneous EEG-fMRI dataset
Author(s): Scrivener, Catriona
Reader, Arran T
Contact Email: arran.reader@stir.ac.uk
Keywords: EEG-fMRI
electrode positions
EEG cap
gel artifact
TMS neuro-navigation
Issue Date: Feb-2022
Date Deposited: 18-Jan-2022
Citation: Scrivener C & Reader AT (2022) Variability of EEG electrode positions and their underlying brain regions: visualizing gel artifacts from a simultaneous EEG-fMRI dataset. Brain and Behavior, 12 (2), Art. No.: e2476. https://doi.org/10.1002/brb3.2476
Abstract: Introduction We investigated the between-subject variability of EEG (electroencephalography) electrode placement from a simultaneously recorded EEG-fMRI (functional magnetic resonance imaging) dataset. Methods Neuro-navigation software was used to localize electrode positions, made possible by the gel artifacts present in the structural magnetic resonance images. To assess variation in the brain regions directly underneath electrodes we used MNI coordinates, their associated Brodmann areas, and labels from the Harvard-Oxford Cortical Atlas. We outline this relatively simple pipeline with accompanying analysis code. Results In a sample of 20 participants, the mean standard deviation of electrode placement was 3.94 mm in x, 5.55 mm in y, and 7.17 mm in z, with the largest variation in parietal and occipital electrodes. In addition, the brain regions covered by electrode pairs were not always consistent; for example, the mean location of electrode PO7 was mapped to BA18 (secondary visual cortex), whereas PO8 was closer to BA19 (visual association cortex). Further, electrode C1 was mapped to BA4 (primary motor cortex), whereas C2 was closer to BA6 (premotor cortex). Conclusions Overall, the results emphasize the variation in electrode positioning that can be found even in a fixed cap. This may be particularly important to consider when using EEG positioning systems to inform non-invasive neurostimulation.
DOI Link: 10.1002/brb3.2476
Rights: © 2022 The Authors. Brain and Behavior published by Wiley Periodicals LLC This is an open access article under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Licence URL(s): http://creativecommons.org/licenses/by/4.0/

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