Variability of EEG Electrode Positions and Their Underlying Brain Regions: Visualizing Gel Artifacts from a Simultaneous EEG-fMRI Dataset

Overview

Journal Brain Behav

Specialty Psychology

Date 2022 Jan 18

PMID 35040596

Authors

Catriona L Scrivener

Arran T Reader

Affiliations

Soon will be listed here.

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.

Citing Articles

EEG connectivity and BDNF correlates of fast motor learning in laparoscopic surgery.

Omurtag A, Sunderland C, Mansfield N, Zakeri Z Sci Rep. 2025; 15(1):7399.

PMID: 40032953 PMC: 11876304. DOI: 10.1038/s41598-025-89261-0.

Reevaluating the neural noise in dyslexia using biomarkers from electroencephalography and high-resolution magnetic resonance spectroscopy.

Glica A, Wasilewska K, Jurkowska J, Zygierewicz J, Kossowski B, Jednorog K Elife. 2025; 13.

PMID: 40029268 PMC: 11875536. DOI: 10.7554/eLife.99920.

A Machine-Learning-Based Analysis of Resting State Electroencephalogram Signals to Identify Latent Schizotypal and Bipolar Development in Healthy University Students.

Gubics F, Nagy A, Dombi J, Palfi A, Szabo Z, Viharos Z Diagnostics (Basel). 2025; 15(4).

PMID: 40002604 PMC: 11854578. DOI: 10.3390/diagnostics15040454.

Disruption of functional network development in children with prenatal Zika virus exposure revealed by resting-state EEG.

Omurtag A, Abdulbaki S, Thesen T, Waechter R, Landon B, Evans R Sci Rep. 2025; 15(1):6346.

PMID: 39984594 PMC: 11845516. DOI: 10.1038/s41598-025-90860-0.

Heterochronous laminar maturation in the human prefrontal cortex.

Sydnor V, Petrie D, McKeon S, Famalette A, Foran W, Calabro F bioRxiv. 2025; .

PMID: 39975178 PMC: 11838308. DOI: 10.1101/2025.01.30.635751.

References

Butler R, Gilbert G, Descoteaux M, Bernier P, Whittingstall K . Application of polymer sensitive MRI sequence to localization of EEG electrodes. J Neurosci Methods. 2016; 278:36-45. DOI: 10.1016/j.jneumeth.2016.12.013. View

Khosla D, Don M, Kwong B . Spatial mislocalization of EEG electrodes -- effects on accuracy of dipole estimation. Clin Neurophysiol. 1999; 110(2):261-71. DOI: 10.1016/s0013-4694(98)00121-7. View

Lamm C, Windischberger C, Leodolter U, Moser E, Bauer H . Co-registration of EEG and MRI data using matching of spline interpolated and MRI-segmented reconstructions of the scalp surface. Brain Topogr. 2002; 14(2):93-100. DOI: 10.1023/a:1012988728672. View

Bhutada A, Sepulveda P, Torres R, Ossandon T, Ruiz S, Sitaram R . Semi-Automated and Direct Localization and Labeling of EEG Electrodes Using MR Structural Images for Simultaneous fMRI-EEG. Front Neurosci. 2021; 14:558981. PMC: 7783406. DOI: 10.3389/fnins.2020.558981. View

Okamoto M, Dan H, Sakamoto K, Takeo K, Shimizu K, Kohno S . Three-dimensional probabilistic anatomical cranio-cerebral correlation via the international 10-20 system oriented for transcranial functional brain mapping. Neuroimage. 2004; 21(1):99-111. DOI: 10.1016/j.neuroimage.2003.08.026. View

Dalal S, Rampp S, Willomitzer F, Ettl S . Consequences of EEG electrode position error on ultimate beamformer source reconstruction performance. Front Neurosci. 2014; 8:42. PMC: 3949288. DOI: 10.3389/fnins.2014.00042. View

De Witte S, Klooster D, Dedoncker J, Duprat R, Remue J, Baeken C . Left prefrontal neuronavigated electrode localization in tDCS: 10-20 EEG system versus MRI-guided neuronavigation. Psychiatry Res Neuroimaging. 2018; 274:1-6. DOI: 10.1016/j.pscychresns.2018.02.001. View

Marino M, Liu Q, Brem S, Wenderoth N, Mantini D . Automated detection and labeling of high-density EEG electrodes from structural MR images. J Neural Eng. 2016; 13(5):056003. DOI: 10.1088/1741-2560/13/5/056003. View

Scrivener C, Reader A . Variability of EEG electrode positions and their underlying brain regions: visualizing gel artifacts from a simultaneous EEG-fMRI dataset. Brain Behav. 2022; 12(2):e2476. PMC: 8865144. DOI: 10.1002/brb3.2476. View

10.

Jurcak V, Tsuzuki D, Dan I . 10/20, 10/10, and 10/5 systems revisited: their validity as relative head-surface-based positioning systems. Neuroimage. 2007; 34(4):1600-11. DOI: 10.1016/j.neuroimage.2006.09.024. View

11.

Steinmetz H, Furst G, Meyer B . Craniocerebral topography within the international 10-20 system. Electroencephalogr Clin Neurophysiol. 1989; 72(6):499-506. DOI: 10.1016/0013-4694(89)90227-7. View

12.

Makris N, Goldstein J, Kennedy D, Hodge S, Caviness V, Faraone S . Decreased volume of left and total anterior insular lobule in schizophrenia. Schizophr Res. 2006; 83(2-3):155-71. DOI: 10.1016/j.schres.2005.11.020. View

13.

Koessler L, Maillard L, Benhadid A, Vignal J, Felblinger J, Vespignani H . Automated cortical projection of EEG sensors: anatomical correlation via the international 10-10 system. Neuroimage. 2009; 46(1):64-72. DOI: 10.1016/j.neuroimage.2009.02.006. View

14.

Frazier J, Chiu S, Breeze J, Makris N, Lange N, Kennedy D . Structural brain magnetic resonance imaging of limbic and thalamic volumes in pediatric bipolar disorder. Am J Psychiatry. 2005; 162(7):1256-65. DOI: 10.1176/appi.ajp.162.7.1256. View

15.

De Munck J, van Houdt P, Verdaasdonk R, Ossenblok P . A semi-automatic method to determine electrode positions and labels from gel artifacts in EEG/fMRI-studies. Neuroimage. 2011; 59(1):399-403. DOI: 10.1016/j.neuroimage.2011.07.021. View

16.

Jurcak V, Okamoto M, Singh A, Dan I . Virtual 10-20 measurement on MR images for inter-modal linking of transcranial and tomographic neuroimaging methods. Neuroimage. 2005; 26(4):1184-92. DOI: 10.1016/j.neuroimage.2005.03.021. View

17.

Homan R, Herman J, Purdy P . Cerebral location of international 10-20 system electrode placement. Electroencephalogr Clin Neurophysiol. 1987; 66(4):376-82. DOI: 10.1016/0013-4694(87)90206-9. View

18.

Beynel L, Appelbaum L, Luber B, Crowell C, Hilbig S, Lim W . Effects of online repetitive transcranial magnetic stimulation (rTMS) on cognitive processing: A meta-analysis and recommendations for future studies. Neurosci Biobehav Rev. 2019; 107:47-58. PMC: 7654714. DOI: 10.1016/j.neubiorev.2019.08.018. View

19.

Atcherson S, Gould H, Pousson M, Prout T . Variability of electrode positions using electrode caps. Brain Topogr. 2007; 20(2):105-11. DOI: 10.1007/s10548-007-0036-z. View

20.

Kozinska D, Carducci F, Nowinski K . Automatic alignment of EEG/MEG and MRI data sets. Clin Neurophysiol. 2001; 112(8):1553-61. DOI: 10.1016/s1388-2457(01)00556-9. View