What is HED?
HED (Hierarchical Event Descriptors) is a framework for systematically describing both laboratory and real-world events. HED tags are comma-separated path strings. The allowed HED tags are organized in a forest of groups with the roots Event, Item, Sensory presentation, Attribute, Action, Participant, Experiment context, and Paradigm. (You can see more about these tags at https://github.com/BigEEGConsortium/HED-schema/wiki/HED-Schema.)
The goal of HED is to describe precisely the nature of the events of interest occurring in an experiment using a common language, so that the following two things can be accomplished. First, you and/or other researchers can better understand the experience and responses of the participant. Secondly, data analysis and meta-analysis can more easily and flexibly compare events (and responses to events) across datasets and studies to better isolate common “cognitive aspects”.
Event annotation comes in two forms: code-specific and event-specific . In code-specific event annotation, researchers identify a small number of event classes or categories and annotate events by category. In event-specific event annotation, researchers identify events with specific values for continuous parameters and annotate events by the times at which the events occur.
The HED framework has been developed for application to EEG brain imaging, but may also be applied to other brain imaging (MEG, fNIRS), multimodal (a.k.a, mobile brain/body imaging), physiological (ECG, EMG, GSR), or purely behavioral data. HED has recently been adopted as part of the BIDS (Brain Imaging Data Structure, http://bids.neuroimaging.io) standard for brain imaging.
For more information on HED, visit the documentation page.