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Hierarchical Event Descriptors

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Hierarchical Event Descriptors
AbbreviationHED
StatusDraft
Year started2010
Latest versionspecification:3.2.0; standard schema:8.2.0
Related standardsBrain Imaging Data Structure, BIDS
DomainNeuroimaging
LicenseCC-BY 4.0
Websitewww.hedtags.org

Hierarchical Event Descriptors (HED) is a framework and family of controlled vocabularies for annotating the timeline in neuroimaging and behavioral experiments to enable searching, comparing, and extracting data[1]. HED is the event annotation mechanism used by the Brain Imaging Data Structure standard for describing events.

HED development is entirely open-source, and source for all HED resources are housed in the hed-standard organization GitHub repository[2][3]. HED has a standard controlled vocabulary called the standard schema which contains terms that are applicable to most experiments. Recognizing that many fields require specialized terms that are not of general interest, HED now allows user communities to develop specialized vocabularies called library schemas that can be combined with standard schema and with other library schemas to provide a more complete controlled vocabularies for particular subfields. An online HED schema browser is available for viewing all available vocabularies[4].


History

Generation 1 (2010-2013)

HED-1G [5] was initially proposed by Nima Bigdely-Shamlo and released in 2010 as the event annotation mechanism for HeadIT[6] an early public repository of EEG data hosted at the UCSD Swartz Center for Computational Neuroscience |url=https://sccn.ucsd.edu%7C of the Institute for Neural Computation at the University of California San Diego (UCSD). Event annotation was organized around a single vocabulary hierarchy (tree) rooted at Time-Locked Event/. The initial vocabulary also contained elements of the COGPO vocabulary[7]. Users could extend the hierarchy to its deepest (leaf) nodes to provide more details. Several EEG studies were successfully annotated for open-source distribution on HeadIT, and the first analyses applying HED tools were demonstrated. During this period, the concept of event-annotation using HED was also adopted by the CANCTA (Cognition and Neuroergonomics Collaborative Technology Alliance, a ten-year basic science research and technology transition program sponsored by the U.S. Army Research Laboratory (ARL), to better understand interactions of brain and body at work[8].

Generation 2 (2014-2019)

As researchers began to annotate their data, HED infrastructure design limitations and vocabulary gaps became apparent. The HED vocabulary was reorganized in a multi-tree (forest) structure where the individual subtrees each represented a subclass hierarchy [9]. Supporting tools were developed, including a GUI for annotation, online validation, and integration of HED-based event-related analysis in the MATLAB environment, in particular within EEGLAB[10]. The use of tag-grouping parentheses was introduced to group related tags in annotations. Kay Robbins and her students at the University of Texas San Antonio (UTSA) made important contirbutions. A large corpus of EEG data was annotated as part of the CANCTA repository, and several studies were published demonstrating the efficacy of HED annotation in facilitating event-related mega-analyses[11].

Generation 3 (2020- )

In 2020, the HED Working Group lead by Kay Robbins of UTSA and Scott Makeig, director of the Swartz Center at UCSD, was formed and began meeting twice weekly with the goal of enhancing HED to address the significant challenges of event annotation in neuroimaging (and beyond). The release of the 3rd-generation standard HED schema version 8.0.0 (in August 2021) gave HED a truly orthogonal (duplication-free) vocabulary tree with significant enhancements in schema structure, including the addition of value classes and schema properties. HED schema terms are now required to be unique and self-explanatory so that annotators can use single (leaf) terms in place of full path annotations (that HED tools can fill in when performing event search and analysis). For example, annotators can use the single tag /Smile in place of its full-term path: Action/Communicate/Communicate-gesturally/Smile. Tools can automatically convert single leaf terms (Smile) so that event annotations involving the term Communicate-gesturally will find event annotations, including Smile and other facial and limb gestures.

HED is used in BIDS for event annotations and HED annotations are automatically validated as part of the BIDS dataset validation.

Features

Library vocabularies

The recent addition of library schemas to the HED (gen-3) system architecture allows any user community to develop a HED vocabulary extension schema (glossary) to use in annotating events in their data, typically in conjunction with terms from the standard schema. The HED-SCORE library, version 1.0.0, released in January, 2023, translates the SCORE (Standardized Computer-based Organized Reporting of EEG, 2nd Ed.) standard for clinical EEG annotation[12]into a HED schema. Specialized schemas for language and movie annotation are under development.

Event processes

HED (gen-3) is based on and supports the conceptual framework that events are processes with temporal extent having distinct onset and offset times. Event markers (or event phase markers) point to time points of interest to analysis, including event Onset and Offset as well as any intermediate time point(s) of interest (marked by Inset markers). For example, if a sound begins playing at (Onset) time point 1 and ceases to play at (Offset) time point 3, then the sound is playing at intermediate time points as well. An (Inset) marker might be used to mark an intermediate time point of interest - for example, its moment of maximum amplitude or other type of phase transition. This contrasts with the earlier (and still prevalent) conception that event annotations should only point to the time point of event onset (often referred to as its trigger), with event duration also being recorded when convenient. The more complete HED (gen-3) conceptual framework makes possible the development of syntax to build and compute on detailed event annotations for complex experiences (speech, music, video, virtual-reality (VR) and augmented-reality (AR) experiences, etc.).

Search and summary

HED (gen-3) requires that vocabulary terms used in a schema are unique, and uses their location within the schema hierarchies to define subclasses. Thus tools are required to treat Action/Communicate/Communicate-gesturally/Smile and Smile as equivalent and users can can annotate events using path end terms (leaves) with no need to quote the full paths, and can request that a search for a term higher in the term path also return all its child paths. This approach is also used in tools to summarize dataset HED annotations.

References

  1. ^ Robbins, Kay; Truong, Dung; Jones, Alexander; Callanan, Ian; Makeig, Scott (2022-04-01). "Building FAIR functionality: Annotating events in time series data using Hierarchical Event Descriptors (HED)". Neuroinformatics. 20 (2): 463-481. doi:10.1007/s12021-021-09537-4. Retrieved 5 October 2023.
  2. ^ "Hierarchical Event Descriptors (HED)". HED standard organization GitHub site. Retrieved 5 October 2023.
  3. ^ "Hierarchical Event Descriptors (HED)". HED project homepage. Retrieved 5 October 2023.
  4. ^ "HED Schema Browser". Retrieved 5 October 2023.
  5. ^ Bigdely-Shamlo, Nima; Kreutz-Delgado, Kenneth; Robbins, Kay; Miyakoshi, Makoto; Westerfield, Marissa; Bel-Bahar, Tarik; Kothe, Christian; Hsi, Jessica (2013). "Hierarchical Event Descriptor (HED) tags for analysis of event-related EEG studies". 2013 IEEE Global Conference on Signal and Information Processing: 1-4. doi:10.1109/GlobalSIP.2013.6736796. Retrieved 5 October 2023.
  6. ^ "Human Electrophysiology, Anatomic Data and Integrated Tools Resource". HeadIT. Retrieved 5 October 2023.
  7. ^ Turner, Jessica; Laird, Angela (2012). "The Cognitive Paradigm Ontology: Design and application". Neuroinformatics. 10: 57-66. PMID 21643732. Retrieved 5 October 2023.
  8. ^ "The Cognition and Neuroergonomics Collaborative Technology Alliance". CANCTA. Army Research Laboratories. Retrieved 5 October 2023.
  9. ^ Bigdely-Shamlo, Nima; Cockfield, Jeremy; Makeig, Scott; Rognon, Thomas; La Valle, Chris; Miyakoshi, Makoto; Robbins, Kay (2016). "Hierarchical Event Descriptors (HED): Semi-structured tagging for real-world events in large-scale EEG". Frontiers in Neuroinformatics. 10. doi:10.3389/fninf.2016.00042. PMID 27799907. Retrieved 5 October 2023.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  10. ^ Delorme, Arnaud; Mullen, Tim; Kothe, Christian; Akalin Acar, Zeynep; Bigdely-Shamlo, Nima; Vankov, Andrey; Makeig, Scott. "EEGLAB, SIFT, NFT, BCILAB, and ERICA: New tools for advanced EEG processing". Computational Intelligence and Neuroscience. 130714. doi:10.1155/2011/130714. PMID 21687590. Retrieved 5 October 2023.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  11. ^ Bigdely-Shamlo, Nima; Touryan, Jonathan; Ojeda, Alejandro; Kothe, Christian; Mullen, Tim; Robbins, Kay (4 September 2019). "Automated EEG mega-analysis II: Cognitive aspects of event related features". NeuroImage (116054). doi:10.1016/j.neuroimage.2019.116054. PMID 31491523. Retrieved 5 October 2023.
  12. ^ Beniczky, Sandor (November 1, 2017). "Standardized computer-based organized reporting of EEG: SCORE – Second version". Clinical Neurophysiology. 128 (11): 2334–2346. doi:10.1016/j.clinph.2017.07.418. PMID 28838815. Retrieved 5 October 2023.
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