Applying an Archetype-Based Approach to Electroencephalography/Event-Related Potential Experiments in the EEGBase...

Area of Science:

• Biomedical Informatics
• Neuroscience Data Management

Background:

• Electronic health records (EHR) systems often lack specialized structures for experimental data.
• EEGBase is a portal for managing experimental electroencephalography/event-related potential (EEG/ERP) data.
• openEHR offers an archetype-based approach for flexible EHR data modeling.

Purpose of the Study:

• To assess the feasibility of using openEHR for modeling experimental EEG/ERP data within EEGBase.
• To evaluate the reusability of existing openEHR archetypes for this domain.
• To develop new openEHR archetypes and templates for EEG/ERP data management.

Main Methods:

• Determined concepts from EEGBase data and metadata compatible with openEHR.
• Searched the Clinical Knowledge Manager (CKM) for existing archetypes.
• Developed new archetypes and integrated them with the odML electrophysiological terminology.
• Created openEHR templates to structure the archetypes for EEGBase.

Main Results:

• Identified and developed eleven openEHR archetypes for experimental EEG/ERP measurements.
• Six archetypes were reused, one extended, and four newly created.
• Archetypes were organized into templates reflecting EEGBase structure.
• Proposed odML terminology referencing for semantic interoperability.

Conclusions:

• openEHR is a feasible and effective approach for modeling experimental EEG/ERP data.
• The developed archetypes and templates improve data interoperability for research.
• The study highlights the utility of openEHR beyond clinical applications.