Skip to main content



Hierarchical Event Descriptors (HED) is an open standard and supporting ecosystem for describing experimental events, conditions, and experiment organization in a format that is both human- and machine-readable to enable analysis, re-analysis, and meta/mega analysis. HED is particularly relevant for neuroimaging and behavioral experiments where events are a central organizing focus for analysis.


HED Homepage
HED specification
HED on GitHub (src)


  • Hierarchical Event Descriptor library schema for clinical EEG data annotation. (arXiv)

  • Building FAIR functionality: Annotating events in time series data using  Hierarchical Event Descriptors (HED). Neuroinformatics Special Issue Building the NeuroCommons. Neuroinformatics  (2022).

  • Automated EEG mega-analysis II: Cognitive aspects of event related features. NeuroImage. 2019 Sep 4:116054. doi: 10.1016/j.neuroimage.2019.116054, PMID:  31491523.

Supporting software

Supporting software

Specification (DOI) (HTML) (src)

Homepage (src)

HED schemas 
(vocabularies) (viewer) (standard schema DOI) (score schema DOI)

Python codebase (DOI) (HTML) (src)

JavaScript codebase (DOI) (src) (npm)

Documentation (HTML) (src)

Example datasets (src)

Youtube channel

Online tools (online tools) (src)


Usage scenario

 HEDs are useful for: 

  • Performing experiments that acquire data 
  • Data annotation, organization, tagging events, etc. 
  • Applying HED tools to find and analyze data 
  • Tool development 
  • Schema building