Rationale: Intracranial electroencephalography (iEEG) provides a unique opportunity to precisely measure human brain function with implanted electrodes. RAVE (R Analysis and Visualization of iEEG) is a comprehensive toolbox for analyzing iEEG datasets. RAVE includes the YAEL package (Your Advanced Electrode Localizer) for automatically localizing electrodes and defining their location in standard space in relation to a variety of atlases. Recently there is an increasing interest in developing new types of electrodes and incorporating more data types such as brain anatomy. In this project, our goal is to support next-generation electrodes and easily incorporate data from other modalities.
Methods: RAVE & YAEL are free and open source, written in R, Python, and JavaScript, and can be installed in minutes on Mac, Windows, and Linux platforms. User interactions occur through a web browser ensuring a familiar user experience and consistent operation across platforms.
Results:
Support for next-generation electrodes: New generations of electrodes have many-fold more electrodes than previously, often with complex geometries, such as the thin-film micro-ECoG array with 1024 contacts. However, these advanced electrodes often feature small contacts that are challenging to detect on CT scans. YAEL allows users to define arbitrarily complex electrode geometries and localize the location of the grid via macro contacts or other landmarks. YAEL can then fit the entire electrode array and display it on the cortical surface model. All data generated by YAEL are stored in common open-source formats that can be easily imported into other software programs.
Support for arbitrary 2D and 3D datasets and ROIs: RAVE's new drag-and-drop feature means that any 2D or 3D datasets (such as fMRI activations, custom atlases, or curvature information about a cortical surface) can be displayed alongside iEEG data. For example, users can drag & drop NIfTI files containing ROI labels. RAVE automatically creates surface models for each ROI, allowing users to quickly visualize the anatomical locations of the sEEG contacts. RAVE can then export a standalone 3D viewer that contains iEEG data along with all the uploaded data, in a single HTML file. This viewer is compatible with any modern browser and can be shared with other users who do not have RAVE installed.
Conclusions: RAVE and YAEL are powerful tools providing integrated electrode localization and visualizations, promoting efficiency and shareability in both clinical and scientific scenarios. Both packages are open source and freely available from
rave.wiki and
yael.wiki.
Funding: NIH 5U01NS113339-05