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

PURPOSE: The 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. METHODS: The 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. RESULTS: A 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

Srovnávací analýza metod na předzpracování evokovaných potenciálů

Výběr vhodné metody předzpracování pro nezprůměrované evokované potenciály (ERP) má zásadní význam, protože může zlepšit spolehlivost systémů rozhraní mozek-počítač (BCI). Tento článek zkoumá vliv předzpracování na spolehlivost detekce ERP vln. Maximální úspěšnost klasifikace byla dosažena kombinací metod wICA a amplitudové metody na odstranění artefaktů.The choice of a suitable preprocessing method for single-trial event-related potential (ERP) data has fundamental importance because it may improve the efficiency of a brain-computer interface (BCI) system. In order to elaborate on this point, this manuscript investigates the impact of preprocessing on single-trial ERP detection. The maximum classification accuracy was achieved with the combination of amplitude-based artifact rejection and wavelet-enhanced independent component analysis (wICA) methods