What is HED?
HED (for ‘Hierarchical Event Descriptors’) is a framework for systematically describing laboratory and real-world events using a controlled but extensible vocabulary. HED tags are comma-separated path strings assigned from a tree-structured vocabulary called a HED schema. HED tags give information about experiment organization and detail the nature of each experiment event, thus creating a permanent record accompanying the data for use in any analysis, later re-analysis, or meta-analysis.
HED tags may also be use to annotate to other brain imaging (MEG, fNIRS), multimodal (a.k.a, mobile brain/body imaging), physiological (ECG, EMG, GSR), or purely behavioral experiment data. HED has recently been adopted as part of the BIDS (Brain Imaging Data Structure) standard at the top level, thus becoming a part of the BIDS data saving standards for an increasing number of brain imaging modalities. Annotation, validation, and data search tools using HED are currently available for use online and/or for use in the EEGLAB/MATLAB environment.
How do I start?
Check out the Documentation page to start using HED tools to tag your data.
The HED community
The HED project is an ongoing open-source organization whose repositories can be found at the HED standards Github site. Visit this site for more information on how to join the HED community of developers and users.
HED was originally developed by Nima Bigdely-Shamlo with Scott Makeig under the HeadIT.org project at the 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). Further HED development was supported by The Cognition and Neuroergonomics Collaborative Technology Alliance (CaN CTA) program of U.S Army Research Laboratory (ARL) under Cooperative Agreement Number W911NF-10-2-0022, with strong and continuing contributions by Kay Robbins of the University of Texas San Antonio. HED is currently (2020) maintained by Scott Makeig and Dung Truong at the Swartz Center, UCSD.