RAVE - Reproducible Analysis & Visualization of iEEG

RAVE is free and open-source software for the analysis of intracranial electroencephalogram (iEEG) data, including data collected using strips and grids (electrocorticography, ECoG) and depth electrodes (stereotactic EEG). A sister package to RAVE is the YAEL electrode localization toolkit.

RAVE is easy to use and creates publication-ready figures with absolutely no programming. RAVE can import standard data formats, including Matlab and EDF, and is compatible with BIDS-iEEG. It runs on laptops, lab servers, or in the cloud. Since all user interactions take place through a web browser, the user experience is identical on Mac, Windows and Linux. Data from RAVE can be exported for analysis using other software (click here for a list of iEEG analysis tools). Conversely, outside results can be imported and visualized using RAVE’s visualization engine. RAVE provides templates to make it easy to create GUI-based analyses using the streamlined application programming interface.

Join our growing RAVE-iEEG community on Slack . E-mail slack@rave.wiki for an invitation.

RAVE has been developed since 2017 with funding provided by NIH 5Tf U01NS113339, 1R24MH117529. If you use RAVE for a publication, please cite:

If you use YAEL for electrode localization, please cite:

Installation

Step 1: Install prerequisites:

Step 2: Install RAVE for the First Time

If you have installed RAVE before, please check How to update RAVE.

  1. Open the R application if it is not already open (RStudio may also be used). Copy and paste the following command into the R (or RStudio) console:
    install.packages('ravemanager', repos = 'https://rave-ieeg.r-universe.dev')
    
  2. Copy and paste the following command into the R console:
    ravemanager::install()
    

    Wait until you see the “Done finalizing installations!” message and the R Console command prompt reappears. This may take a few minutes depending on the speed of your internet connection. After installation, it is recommended to close all instances of R and restart R. Common installation problems:

Step 3: Install Isolated Python Environment (optional but recommended)

  • Copy and paste the following command into the R console:
    ravemanager::configure_python()
    

    Some advanced RAVE features (such as CT to MRI alignment via nipy or ants) call Python libraries. To prevent conflicts with existing Python installations and ensure stability and reliability, this step uses miniconda to install an isolated Python environment and useful Python packages (numpy, scipy, jupyterlab, mne, nipy, pynwb, ants). If this step fails, proceed to the next step.

Step 4: Launch RAVE

  • Close all instances of R and restart R. Copy and paste the following command into the R console:
    rave::start_rave2()
    

Publications

Send us your (p)reprint and we will add it to the list!

  1. RAVE_Neuroimage_Cover.jpg
    RAVE: Comprehensive open-source software for reproducible analysis and visualization of intracranial EEG data
    NeuroImage, Dec 2020
  2. YAEL_GraphicalAbstract.jpg
    YAEL: Your Advanced Electrode Localizer
    eNeuro, Oct 2023
  3. Intracranial stimulation and EEG feature analysis reveal affective salience network specialization
    Brian A. Metzger, Prathik Kalva, Madaline M. Mocchi, Brian Cui, and 13 more authors
    Brain: A Journal of Neurology, Oct 2023
  4. Responses to Visual Speech in Human Posterior Superior Temporal Gyrus Examined with iEEG Deconvolution
    Brian A. MetzgerJohn F. MagnottiZhengjia Wang, Elizabeth Nesbitt, and 3 more authors
    The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, Sep 2020
  5. Functional group bridge for simultaneous regression and support estimation
    Biometrics, Jun 2023
  6. The visual speech head start improves perception and reduces superior temporal cortex responses to auditory speech
    Patrick J KarasJohn F MagnottiBrian A Metzger, Lin L Zhu, and 3 more authors
    eLife, Aug 2019
    Publisher: eLife Sciences Publications, Ltd
  7. Imaging versus electrographic connectivity in human mood-related fronto-temporal networks
    Joshua A. Adkinson, Evangelia Tsolaki, Sameer A. ShethBrian A. Metzger, and 14 more authors
    Brain Stimulation, May 2022
  8. Bayesian Image-on-Scalar Regression with a Spatial Global-Local Spike-and-Slab Prior
    Zijian Zeng, Meng Li, and Marina Vannucci
    Bayesian Analysis, Mar 2024
    Publisher: International Society for Bayesian Analysis