Capturing the nature of events and event context using hierarchical event descriptors (HED)

• Published in: NeuroImage (Orlando, Fla.) Vol. 245; p. 118766
• Main Authors: Robbins, Kay, Truong, Dung, Appelhoff, Stefan, Delorme, Arnaud, Makeig, Scott
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.12.2021; Elsevier Limited; Elsevier
• Subjects: Annotations; Automation; Behavior; BIDS; Brain mapping; Case studies; Cognitive science; Data analysis; Datasets; Datasets as Topic; EEG; Electroencephalography - methods; Event annotation; Events; Experiments; Facial Recognition - physiology; Functional magnetic resonance imaging; Functional Neuroimaging - methods; HED; HED-3G; Hierarchical event descriptors; Humans; Magnetoencephalography - methods; Medical imaging; MEG; Memory; Metadata; Neuroimaging; Neurosciences - methods; Phase transitions; Recording sessions; Research Design; Symmetry; Time series; HED-3G; Event annotation; Events; HED; EEG; Time series; BIDS; Hierarchical event descriptors; MEG
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2021.118766

•Events represent experiences or processes that unfold in time, often having distinct phases.•Event markers are identified time points usually associated with a phase transition of an event.•The critical linkage of experimental data to external reality and processes is achieved by creating appropriate event markers and associating these markers with informative metadata.•The HED (Hierarchical event Descriptor) system provides a framework and tools for making this association in a machine-actionable, analysis-ready way.•Without appropriate event design (appropriate event markers and informative annotation) neuroimaging data will not be usable to its full potential by the broader community. Event-related data analysis plays a central role in EEG and MEG (MEEG) and other neuroimaging modalities including fMRI. Choices about which events to report and how to annotate their full natures significantly influence the value, reliability, and reproducibility of neuroimaging datasets for further analysis and meta- or mega-analysis. A powerful annotation strategy using the new third-generation formulation of the Hierarchical Event Descriptors (HED) framework and tools (hedtags.org) combines robust event description with details of experiment design and metadata in a human-readable as well as machine-actionable form, making event annotation relevant to the full range of neuroimaging and other time series data. This paper considers the event design and annotation process using as a case study the well-known multi-subject, multimodal dataset of Wakeman and Henson made available by its authors as a Brain Imaging Data Structure (BIDS) dataset (bids.neuroimaging.io). We propose a set of best practices and guidelines for event annotation integrated in a natural way into the BIDS metadata file architecture, examine the impact of event design decisions, and provide a working example of organizing events in MEEG and other neuroimaging data. We demonstrate how annotations using HED can document events occurring during neuroimaging experiments as well as their interrelationships, providing machine-actionable annotation enabling automated within- and across-experiment analysis and comparisons. We discuss the evolution of HED software tools and have made available an accompanying HED-annotated BIDS-formated edition of the MEEG data of the Wakeman and Henson dataset (openneuro.org, ds003645). [Display omitted]