biology

Hierarchical Event Descriptors

Hierarchical Event Descriptors is a biology topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Hierarchical Event Descriptors rather than just read about it. In short: Hierarchical Event Descriptors (HED) is a conceptual and software framework that includes a family of controlled vocabularies for annotating experimental metadata and experienced events on the timeline of neuroimaging and behavioral experiments. The goal of HED is to standardize annotations and the mechanisms for handling these annotations to enable searching, comparing, and extracting data of interest for analysis.

Key takeaways

Hierarchical Event Descriptors belongs to biology; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Hierarchical Event Descriptors to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Hierarchical Event Descriptors from memory before moving on to harder problems.

Reference excerpt

Hierarchical Event Descriptors (HED) is a conceptual and software framework that includes a family of controlled vocabularies for annotating experimental metadata and experienced events on the timeline of neuroimaging and behavioral experiments. The goal of HED is to standardize annotations and the mechanisms for handling these annotations to enable searching, comparing, and extracting data of interest for analysis. HED is the event annotation mechanism used by the Brain Imaging Data Structure (BIDS) standard for describing events. HED development is open, and source for all HED resources is housed in the hed-standard organization GitHub repository. HED has a base controlled vocabulary called the standard schema that contains terms applicable to most experiments. Recognizing that many fields require specialized terms not of general interest, HED also allows user communities to develop specialized vocabularies termed library schemas that can be combined with the standard schema and with other library schemas to provide a controlled vocabulary for experiments in a subfield. An online HED schema browser is available for viewing all available vocabularies.

History

Generation 1 (2010-2013) HED-1G was initially proposed by Nima Bigdely-Shamlo and released in 2010 as the event annotation mechanism for HeadIT an early public repository of EEG data hosted at the UCSD Swartz Center for Computational Neuroscience 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. 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.

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. 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. The use of tag-grouping parentheses was introduced to group related tags in annotations. 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

Generation 3 (2020- ) In 2020, the HED Working Group was formed 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 an 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 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. By contrast, event markers (or event phase markers) designate time points. Markers associated with Onset and Offset tags, as well markers of intermediate time point(s) of interest (associated with Inset tags) allow tools to extract event processes as they unfold. For example, if a sound begins to play at (Onset) time point 1 and ceases to play at (Offset) time point 3, tools can infer that the sound is playing at intermediate time points as well. Inset tags may be used to mark intermediate time points of interest - for example, the sound's moment of maximum amplitude or other types of phase transition. The more complete HED (gen-3) conceptual framework makes possible the development of syntax to build and to compute on detailed event annotations for complex experiences (of speech, music, video, and in virtual-reality (VR) and/or augmented-reality (AR), 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 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 children. This approach is also used in tools to summarize dataset HED annotations.

References

Illustrations

Worked examples

Example 1 — a first encounter with Hierarchical Event Descriptors

Start with the simplest possible case. Write down what Hierarchical Event Descriptors claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In biology, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Hierarchical Event Descriptors before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Hierarchical Event Descriptors ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Hierarchical Event Descriptors

In research: Hierarchical Event Descriptors appears in biology research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Hierarchical Event Descriptors in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Hierarchical Event Descriptors is common in secondary-school and first-year university syllabi. It links to neighbouring topics Neuroimaging software, so understanding it makes those chapters shorter.
In everyday life: Look for Hierarchical Event Descriptors outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Hierarchical Event Descriptors in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Hierarchical Event Descriptors means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Hierarchical Event Descriptors out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Hierarchical Event Descriptors in simple terms?

Why does Hierarchical Event Descriptors matter?

Because it connects several biology ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Hierarchical Event Descriptors?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Hierarchical Event Descriptors.