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Hierarchical Event Descriptors (HED): Semi-Structured Tagging for Real-World Events in Large-Scale EEG

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 ve...

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Autores principales: Bigdely-Shamlo, Nima, Cockfield, Jeremy, Makeig, Scott, Rognon, Thomas, La Valle, Chris, Miyakoshi, Makoto, Robbins, Kay A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5065975/
https://www.ncbi.nlm.nih.gov/pubmed/27799907
http://dx.doi.org/10.3389/fninf.2016.00042
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author Bigdely-Shamlo, Nima
Cockfield, Jeremy
Makeig, Scott
Rognon, Thomas
La Valle, Chris
Miyakoshi, Makoto
Robbins, Kay A.
author_facet Bigdely-Shamlo, Nima
Cockfield, Jeremy
Makeig, Scott
Rognon, Thomas
La Valle, Chris
Miyakoshi, Makoto
Robbins, Kay A.
author_sort Bigdely-Shamlo, Nima
collection PubMed
description 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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spelling pubmed-50659752016-10-31 Hierarchical Event Descriptors (HED): Semi-Structured Tagging for Real-World Events in Large-Scale EEG Bigdely-Shamlo, Nima Cockfield, Jeremy Makeig, Scott Rognon, Thomas La Valle, Chris Miyakoshi, Makoto Robbins, Kay A. Front Neuroinform Neuroscience 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. Frontiers Media S.A. 2016-10-17 /pmc/articles/PMC5065975/ /pubmed/27799907 http://dx.doi.org/10.3389/fninf.2016.00042 Text en Copyright © 2016 Bigdely-Shamlo, Cockfield, Makeig, Rognon, La Valle, Miyakoshi and Robbins. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Bigdely-Shamlo, Nima
Cockfield, Jeremy
Makeig, Scott
Rognon, Thomas
La Valle, Chris
Miyakoshi, Makoto
Robbins, Kay A.
Hierarchical Event Descriptors (HED): Semi-Structured Tagging for Real-World Events in Large-Scale EEG
title Hierarchical Event Descriptors (HED): Semi-Structured Tagging for Real-World Events in Large-Scale EEG
title_full Hierarchical Event Descriptors (HED): Semi-Structured Tagging for Real-World Events in Large-Scale EEG
title_fullStr Hierarchical Event Descriptors (HED): Semi-Structured Tagging for Real-World Events in Large-Scale EEG
title_full_unstemmed Hierarchical Event Descriptors (HED): Semi-Structured Tagging for Real-World Events in Large-Scale EEG
title_short Hierarchical Event Descriptors (HED): Semi-Structured Tagging for Real-World Events in Large-Scale EEG
title_sort hierarchical event descriptors (hed): semi-structured tagging for real-world events in large-scale eeg
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5065975/
https://www.ncbi.nlm.nih.gov/pubmed/27799907
http://dx.doi.org/10.3389/fninf.2016.00042
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