Automatic Detection of Fiducial Landmarks Toward the Development of an Application for Digitizing the Locations of EEG Electrodes: Occipital Structure Sensor-Based Work

Abstract

The reconstruction of electrophysiological sources within the brain is sensitive to the constructed head model, which depends on the positioning accuracy of anatomical landmarks known as fiducials. In this work, we propose an algorithm for the automatic detection of fiducial landmarks of EEG electrodes on the 3D human head model. Our proposal combines a dimensional reduction approach with a perspective projection from 3D to 2D object space; the eye and ear automatic detection in a 2D face image by two cascades of classifiers and geometric transformations to obtain 3D spatial coordinates of the landmarks and to generate the head coordinate system, This is accomplished by considering the characteristics of the scanner information. Capturing the 3D model of the head is done with Occipital Inc. ST01 structure sensor and the implementation of our algorithm was carried out on MATLAB R2018b using the Computer Vision Toolbox and the FieldTrip Toolbox. The experimental results were aimed at recursively exploring the efficacy of the facial feature detectors as a function of the projection angle; they show that robust results are obtained in terms of false acceptance rate. Our proposal is an initial step of an approach for the automatic digitization of electrode locations. The experimental results demonstrate that the proposed method detects anatomical facial landmarks automatically, accurately, and rapidly.

EEG Electrodes and Where to Find Them: Automated Localization From 3D Scans

“…Introducing preprocessing steps, such as light correction and general mesh improvement strategies, could potentially reduce the complexity of the input data, which might not fully test the algorithm's robustness in handling diverse conditions. A notable constraint of our approach is its inapplicability to retrospective data, making alternative strategies like those suggested by Martinez et al [32], which employ automatic localization of landmarks through algorithms for eyes, ears, and facial recognition, more suitable. However, our system incorporates a validation tool that facilitates rapid corrections of incorrectly positioned landmarks and allows for manual placement as well.…”