Algorithmic Localization of High-density EEG Electrode Positions Using Motion Capture

Background: 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.

New Method: 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.

Results: 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.

Comparison With Existing Methods: 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.

Conclusions: 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.