ArticleFrontiers in neuroinformatics2016
Hierarchical Event Descriptors (HED): Semi-Structured Tagging for Real-World Events in Large-Scale EEG.
Article in Frontiers in neuroinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed.
- Dataset of audiovisual unscripted monologues with speech annotations.Data in brief · 2026Article
- Review
- Hierarchical Event Descriptor library schema for EEG data annotation.Scientific data · 2025Article
- Article
- EPAT: a user-friendly MATLAB toolbox for EEG/ERP data processing and analysis.Frontiers in neuroinformatics · 2024Article
- Non-invasive brain imaging to advance the understanding of human balance.Current opinion in biomedical engineering · 2023Article
- FAIR human neuroscientific data sharing to advance AI driven research and applications: Legal frameworks and missing metadata standards.Frontiers in genetics · 2023Review
- Building FAIR Functionality: Annotating Events in Time Series Data Using Hierarchical Event Descriptors (HED).Neuroinformatics · 2022Article
- Dementia ConnEEGtome: Towards multicentric harmonization of EEG connectivity in neurodegeneration.International journal of psychophysiology : official journal of the International Organization of Psychophysiology · 2022Review
- Capturing the nature of events and event context using hierarchical event descriptors (HED).NeuroImage · 2021Article
- Article
- High-density EEG mobile brain/body imaging data recorded during a challenging auditory gait pacing task.Scientific data · 2019Article
- EEG-BIDS, an extension to the brain imaging data structure for electroencephalography.Scientific data · 2019Article
- Workshops of the Seventh International Brain-Computer Interface Meeting: Not Getting Lost in Translation.Brain computer interfaces (Abingdon, England) · 2019Article
- An 18-subject EEG data collection using a visual-oddball task, designed for benchmarking algorithms and headset performance comparisons.Data in brief · 2018Article
- A Review of Issues Related to Data Acquisition and Analysis in EEG/MEG Studies.Brain sciences · 2017Review
Corrections and comments
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Authors and funding
7 authors.
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Abstract
Real-world brain imaging by EEG requires accurate annotation of complex subject-environment interactions in event-rich tasks and paradigms. This paper describes the evolution of the Hierarchical Event Descriptor (HED) system for systematically describing both laboratory and real-world events. HED version 2, first described here, provides the semantic capability of describing a variety of subject and environmental states. HED descriptions can include stimulus presentation events on screen or in virtual worlds, experimental or spontaneous events occurring in the real world environment, and events experienced via one or multiple sensory modalities. Furthermore, HED 2 can distinguish between the mere presence of an object and its actual (or putative) perception by a subject. Although the HED framework has implicit ontological and linked data representations, the user-interface for HED annotation is more intuitive than traditional ontological annotation. We believe that hiding the formal representations allows for a more user-friendly interface, making consistent, detailed tagging of experimental, and real-world events possible for research users. HED is extensible while retaining the advantages of having an enforced common core vocabulary. We have developed a collection of tools to support HED tag assignment and validation; these are available at hedtags.org. A plug-in for EEGLAB (sccn.ucsd.edu/eeglab), CTAGGER, is also available to speed the process of tagging existing studies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.