Towards Deep Learning-Based EEG Electrode Detection Using Automatically Generated Labels
This work addresses the need for efficient and cost-effective electrode positioning in EEG systems, particularly for inverse localization, though it is incremental as it applies existing deep learning methods to a new domain with automated labeling.
The paper tackles the problem of time-consuming and expensive EEG electrode localization by proposing a deep learning-based method using an RGBD camera, achieving a mean absolute error of 5.69 ± 6.1mm for electrode detection.
Electroencephalography (EEG) allows for source measurement of electrical brain activity. Particularly for inverse localization, the electrode positions on the scalp need to be known. Often, systems such as optical digitizing scanners are used for accurate localization with a stylus. However, the approach is time-consuming as each electrode needs to be scanned manually and the scanning systems are expensive. We propose using an RGBD camera to directly track electrodes in the images using deep learning methods. Studying and evaluating deep learning methods requires large amounts of labeled data. To overcome the time-consuming data annotation, we generate a large number of ground-truth labels using a robotic setup. We demonstrate that deep learning-based electrode detection is feasible with a mean absolute error of 5.69 +- 6.1mm and that our annotation scheme provides a useful environment for studying deep learning methods for electrode detection.