Improving neuroimaging headgear placement robustness using facial-landmark-guided augmented reality

Accurate and consistent probe placement is crucial in functional near-infrared spectroscopy (fNIRS) and electroencephalogram (EEG) experiments, especially in longitudinal and group-based studies. Both operator experience and subject head shape variability can affect placement accuracy.

We aim to develop an easy-to-use software, NeuroNavigatAR (NNAR), utilizing augmented reality (AR) and machine learning to estimate and display in real-time the subject's cranial and head landmarks to guide consistent headgear placement.

By applying a facial recognition toolbox to the image frames extracted from a video camera, we can obtain and continuously track subject-specific three-dimensional facial landmarks. Separately, we have precomputed a robust linear transformation between facial landmarks and key cranial landmarks, including nasion and preauricular points, using a large public head-model library consisting of over 1000 subjects. These allow us to rapidly estimate subject-specific cranial landmarks and subsequently render atlas-derived head landmarks to the subject's camera stream.

An open-source graphical user interface implementing this AR system has achieved a speed of 15 frames per second using a laptop. A median 10-20 position error of 1.52 cm was found when using a general adult atlas and is further reduced to 1.33 cm and 0.75 cm when using age-matched atlas models and subject-specific head surfaces, respectively. NNAR demonstrated consistent head-landmark prediction errors across repeated measurement sessions; there is also no statistically significant difference in accuracy across age groups.

NNAR is an easy-to-use AR headgear placement monitoring tool that is expected to significantly enhance consistency and reduce setup time for fNIRS and EEG probe donning across a wide range of studies.