Algorithmic localization of high-density EEG electrode positions using motion capture.

Abstract 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.