HED tags are assigned to event codes (also known as triggers or event numbers) in EEG recordings and allow humans and computers to better understand what these codes represent (e.g. code #5 -> Target detection in an RSVP paradigm). Click here to see an interactive visualization of HED hierarchy.

Why tags?

In the same way that we tag a picture on Flicker, or a video clip on Youtube (e.g. cat, cute, funny), we can tag EEG experimental event types used in event-related EEG research. Hierarchical Event Descriptors (HED) is a set of descriptor tags partially adopted from BrainMap/NeuroLex ontologies and organized hierarchically. HED tags can be used to describe many types of EEG experiment events in a uniform, extensible, and machine readable manner.

How do I start?

Follow these steps:

  1. Read HED Schema. It contains all tags in the HED hierarchy.
  2. Read HED reference paper.

  3. Read HED Tagging Strategy Guide

  4. Read about CTAGGER GUI-based HED tagging (MATLAB, Standalone) and validation tools.


  1. CTAGGER is a MATLAB/Java toolbox that implements a user-friendly, semi-structured and expandable strategy for event annotation in dynamic brain imaging and other time-series. You can use this tool to assign HED tags to your events (e.g. in EEGLAB). Here are the link for MATLAB and Java versions of CTAGGER.

  2. HED conversion utilites is a GUI in Python that contains the following functionality

    • Convert an HED hierarchy from formatted .txt to an xml formatted file.
    • Verify the HED hierarchy in xml form against an xml schema.
    • Verify formatted tags against an HED hierarchy.
    • Create a file that shows a mapping from one HED hierarchy to another.
  3. rERP Toolbox is an open source Matlab toolbox for calculating overlapping Event Related Potentials (ERP) by multiple regression (an alternative to averaging). It can also perform regression on HED tags.

  4. Interactive visualization of HED hierarchy in your web browser.

Who is using HED tags?


and many others.


HED was originally developed under HeadIT project at Swartz Center for Computational Neuroscience (SCCN) of the University of California, San Diego and funded by U.S. National Institutes of Health grants R01-MH084819 (Makeig, Grethe PIs) and R01-NS047293 (Makeig PI). HED development is now supported by The Cognition and Neuroergonomics Collaborative Technology Alliance (CaN CTA) program of U.S Army Research Labaratory.