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Hierarchical Event Descriptor library schema for EEG data annotation
Dora Hermes1, Tal Pal Attia2, Sándor Beniczky3,4
1Multimodal Neuroimaging Laboratory, Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA. hermes.dora@mayo.edu.
Scientific Data
|August 19, 2025
Summary
Standardizing electrophysiological event annotation with the new HED-SCORE library schema enhances machine readability for computational neuroscience and clinical applications. This framework improves data exploration and automated analysis of human electrophysiological data.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Biomedical Informatics
Background:
- Standardizing terminology for electrophysiological events is crucial for advancing computational research and clinical care.
- Machine readability of annotations is essential for automated analysis of electrophysiological data.
- Existing frameworks like Hierarchical Event Descriptor (HED) lack electrophysiological terms, and Standardized Computer-based Organized Reporting of EEG (SCORE) is not machine-readable.
Purpose of the Study:
- To develop a machine-readable HED library schema for SCORE terms.
- To extend the HED schema with a controlled vocabulary for electrophysiological events.
- To enable standardized annotation and computation on human electrophysiological data.
Main Methods:
- Developed the HED-SCORE library schema by integrating SCORE terms into the HED framework.
- Ensured machine readability and searchability of the SCORE terms.
- Demonstrated the schema's application using electrophysiological data within the Brain Imaging Data Structure (BIDS).
Main Results:
- Successfully created the HED-SCORE library schema, making SCORE terms machine-readable.
- The schema effectively annotates electrophysiological events in EEG data.
- The HED-SCORE library schema is compatible with the BIDS data structure.
Conclusions:
- The HED-SCORE library schema provides a standardized, machine-readable method for annotating electrophysiological events.
- This facilitates data exploration, computational analysis, and clinical applications of human electrophysiological data.
- Enables global collaboration and advancement in the analysis of neuroscience data.
