{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Hirth LN"],"funding":["Intramural NIH HHS","Intramural Research Program of the NIH Clinical Center"],"pagination":["108919"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9677341"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["346"],"pubmed_abstract":["<h4>Background</h4>Accurate source localization from electroencephalography (EEG) requires electrode co-registration to brain anatomy, a process that depends on precise measurement of 3D scalp locations. Stylus digitizers and camera-based scanners for such measurements require the subject to remain still and therefore are not ideal for young children or those with movement disorders.<h4>New method</h4>Motion capture accurately measures electrode position in one frame but marker placement adds significant setup time, particularly in high-density EEG. We developed an algorithm, named MoLo and implemented as an open-source MATLAB toolbox, to compute 3D electrode coordinates from a subset of positions measured in motion capture using spline interpolation. Algorithm accuracy was evaluated across 5 different-sized head models.<h4>Results</h4>MoLo interpolation reduced setup time by approximately 10 min for 64-channel EEG. Mean electrode interpolation error was 2.95 ± 1.3 mm (range: 0.38-7.98 mm). Source localization errors with interpolated compared to true electrode locations were below 1 mm and 0.1 mm in 75 % and 35 % of dipoles, respectively.<h4>Comparison with existing methods</h4>MoLo location accuracy is comparable to stylus digitizers and camera-scanners, common in clinical research. The MoLo algorithm could be deployed with other tools beyond motion capture, e.g., a stylus, to extract high-density EEG electrode locations from a subset of measured positions. The algorithm is particularly useful for research involving young children and others who cannot remain still for extended time periods.<h4>Conclusions</h4>Electrode position and source localization errors with MoLo are similar to other modalities supporting its use to measure high-density EEG electrode positions in research and clinical settings."],"journal":["Journal of neuroscience methods"],"pubmed_title":["Algorithmic localization of high-density EEG electrode positions using motion capture."],"pmcid":["PMC9677341"],"funding_grant_id":["ZIA CL090084"],"pubmed_authors":["Hirth LN","Stanley CJ","Bulea TC","Damiano DL"],"additional_accession":[]},"is_claimable":false,"name":"Algorithmic localization of high-density EEG electrode positions using motion capture.","description":"<h4>Background</h4>Accurate source localization from electroencephalography (EEG) requires electrode co-registration to brain anatomy, a process that depends on precise measurement of 3D scalp locations. Stylus digitizers and camera-based scanners for such measurements require the subject to remain still and therefore are not ideal for young children or those with movement disorders.<h4>New method</h4>Motion capture accurately measures electrode position in one frame but marker placement adds significant setup time, particularly in high-density EEG. We developed an algorithm, named MoLo and implemented as an open-source MATLAB toolbox, to compute 3D electrode coordinates from a subset of positions measured in motion capture using spline interpolation. Algorithm accuracy was evaluated across 5 different-sized head models.<h4>Results</h4>MoLo interpolation reduced setup time by approximately 10 min for 64-channel EEG. Mean electrode interpolation error was 2.95 ± 1.3 mm (range: 0.38-7.98 mm). Source localization errors with interpolated compared to true electrode locations were below 1 mm and 0.1 mm in 75 % and 35 % of dipoles, respectively.<h4>Comparison with existing methods</h4>MoLo location accuracy is comparable to stylus digitizers and camera-scanners, common in clinical research. The MoLo algorithm could be deployed with other tools beyond motion capture, e.g., a stylus, to extract high-density EEG electrode locations from a subset of measured positions. The algorithm is particularly useful for research involving young children and others who cannot remain still for extended time periods.<h4>Conclusions</h4>Electrode position and source localization errors with MoLo are similar to other modalities supporting its use to measure high-density EEG electrode positions in research and clinical settings.","dates":{"release":"2020-01-01T00:00:00Z","publication":"2020 Dec","modification":"2025-04-05T10:57:20.329Z","creation":"2025-04-05T10:57:20.329Z"},"accession":"S-EPMC9677341","cross_references":{"pubmed":["32853593"],"doi":["10.1016/j.jneumeth.2020.108919"]}}