Hierarchical Event Descriptor library schema for EEG data annotation

TL;DR

This paper introduces a hierarchical schema for annotating EEG data with standardized, machine-readable terms, enhancing data sharing, analysis, and clinical research across neuroscience and medical fields.

Contribution

It extends the HED framework with a SCORE-specific library schema, enabling consistent, detailed annotation of EEG events including clinical artifacts.

Findings

HED-SCORE schema is compatible with BIDS EEG data.

Enables standardized, machine-readable annotations for clinical and research EEG data.

Facilitates data sharing and computational analysis across platforms.

Abstract

Standardizing terminology to annotate electrophysiological events can improve both computational research and clinical care. Sharing data enriched with standard terms can facilitate data exploration, from case studies to mega-analyses. The machine readability of such electrophysiological event annotations is essential for performing analyses efficiently across software tools and packages. Hierarchical Event Descriptors (HED) provide a framework for describing events in neuroscience experiments. HED library schemas extend the standard HED schema vocabulary to include specialized vocabularies, such as standardized clinical terms for electrophysiological events. The Standardized Computer-based Organized Reporting of EEG (SCORE) defines terms for annotating EEG events, including artifacts. This study developed a HED library schema for SCORE, making the terms machine-readable. We demonstrate that the HED-SCORE library schema can be used to annotate events in EEG data stored in the Brain Imaging Data Structure (BIDS). Clinicians and researchers worldwide can now use the HED-SCORE library schema to annotate and compute on electrophysiological data obtained from the human brain.