Rationale:
Intracranial electroencephalography (iEEG) provides a unique opportunity to measure human brain function with implanted electrodes, both stereotactic EEG (sEEG) and subdural electrodes (SDE) arrayed as grids or strips . A key step in neuroscience inference from iEEG is localizing the electrodes relative to individual subject anatomy and identified regions in brain atlases. While there are number of workflows for electrode localization, most suffer from one or more limitations. The first limitation is a lack of integration: scientists must install and use different software packages for each localization step. Secondly, they are inefficient. While most iEEG analysis steps can be automated, electrode localization is still largely a manual process. Third, most current tools are limited to the localization process itself leaving users without the ability to create high-quality visualizations for clinical and research purposes. We developed YAEL (Your Advanced Electrode Localizer) to overcome these limitations.
Methods:
YAEL contains more than 30,000 lines of code, is free and open source, and can be installed in minutes on Mac, Windows, and Linux platforms from
https://yael.wiki. User interactions with YAEL occur through a web browser ensuring a familiar user experience and consistent operation across platforms and whether YAEL is used locally or deployed in the cloud.
Results:
YAEL is completely integrated, easy-to-use graphical user interface (GUI) that controls every aspect of image registration and electrode localization. YAEL includes a flexible 3D viewer alongside automation tools to make accurate localization of electrodes quick and easy. After localization is complete, YAEL leverages the same viewer to create high-quality visualizations of electrode data including identified brain areas from atlases; the response to experimental tasks measured with iEEG; and clinical measures such as epileptiform activity or the results of electrical stimulation mapping.
Conclusions:
We developed YAEL to help clinicians and scientists quickly register, localize and visualize intracranial EEG data. The software is freely available and easy to incorporate in many current iEEG workflows.
Funding: NIH NS065395, NS113339, and R24MH117529.