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

Abstract

PurposeThe purpose of this study is to investigate the feasibility of applying openEHR (an archetype-based approach for electronic health records representation) to modeling data stored in EEGBase, a portal for experimental electroencephalography/event-related potential (EEG/ERP) data management. The study evaluates re-usage of existing openEHR archetypes and proposes a set of new archetypes together with the openEHR templates covering the domain. The main goals of the study are to (i) link existing EEGBase data/metadata and openEHR archetype structures and (ii) propose a new openEHR archetype set describing the EEG/ERP domain since this set of archetypes currently does not exist in public repositories.MethodsThe main methodology is based on the determination of the concepts obtained from EEGBase experimental data and metadata that are expressible structurally by the openEHR reference model and semantically by openEHR archetypes. In addition, templates as the third openEHR resource allow us to define constraints over archetypes. Clinical Knowledge Manager (CKM), a public openEHR archetype repository, was searched for the archetypes matching the determined concepts. According to the search results, the archetypes already existing in CKM were applied and the archetypes not existing in the CKM were newly developed. openEHR archetypes support linkage to external terminologies. To increase semantic interoperability of the new archetypes, binding with the existing odML electrophysiological terminology was assured. Further, to increase structural interoperability, also other current solutions besides EEGBase were considered during the development phase. Finally, a set of templates using the selected archetypes was created to meet EEGBase requirements.ResultsA set of eleven archetypes that encompassed the domain of experimental EEG/ERP measurements were identified. Of these, six were reused without changes, one was extended, and four were newly created. All archetypes were arranged in the templates reflecting the EEGBase metadata structure. A mechanism of odML terminology referencing was proposed to assure semantic interoperability of the archetypes. The openEHR approach was found to be useful not only for clinical purposes but also for experimental data modeling.

Understanding the Nature of Metadata: Systematic Review

“…Another vital issue is the divergence of data and the corresponding metadata [ 14 ]: data did not match the metadata and thus was not fit for reuse. The reasons for this were diverse: the lack of transparency of the (meta)data origin [ 47 ] or the boundary between data and metadata was unclear or rather a matter of changed perspective [ 28 ]. A viable approach was the extraction of metadata from the primary information technology systems and to populate it directly [ 23 ].…”

ARTEM-IS for ERP: Agreed Reporting Template for EEG Methodology - International Standard for documenting studies on Event-Related Potentials

“…In addition to projects specifically focused on improving methodological descriptions in papers, other initiatives that aim at facilitating research transparency include an ERP pre-registration template (Govaart et al, 2022), efforts aimed at standardising rather than documenting pipelines (e.g., Bigdely-Shamlo et al, 2015;Gabard-Durnam et al, 2018;Monachino et al, 2021;Rodrigues et al, 2021), and data/metadata sharing standards and platforms (e.g., BIDS -Gorgolewski et al, 2016;Pernet et al, 2019) and the BIDS-compliant data sharing platform OpenNeuro, EEGBase portal (Papež & Mouček, 2017). The pre-registration template can help researchers consider, make and document methodological decisions before conducting a study.…”

Methodology for developing OpenEHR archetypes: a narrative literature review

“…The paper by Papež and Mouček (14) , in this category, deals with the feasibility of applying openEHR to model the data stored in EEGBase, which is a portal for managing experimental data from electroencephalograms and related potential events. The authors evaluated the reuse of existing openEHR archetypes and proposed a set of new archetypes together with openEHR templates from the same domain.…”