Software and hardware infrastructure for research in electrophysiology

Mouček, Roman; Brůha, Petr; Ježek, Petr; Mautner, Pavel; Novotný, Jiří; Papež, Václav; Prokop, Tomáš; Řondík, Tomáš; Štěbeták, Jan; Vařeka, Lukáš

doi:10.3389/fninf.2014.00020

Cited by 20 publications

(20 citation statements)

References 32 publications

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“…4) and related hardware and embedded control software for generation of visual stimuli (lighting up the LED panels) and recording the participant's responses [1] was used. The task of the participant was to press a button near the LED diode panel turned on by right or left hand as quickly as possible.…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Collection of human reaction times and supporting health related data for analysis of cognitive and physical performance

et al. 2018

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Smoking, excessive drinking, overeating and physical inactivity are well-established risk factors decreasing human physical performance. Moreover, epidemiological work has identified modifiable lifestyle factors, such as poor diet and physical and cognitive inactivity that are associated with the risk of reduced cognitive performance. Definition, collection and annotation of human reaction times and suitable health related data and metadata provides researchers with a necessary source for further analysis of human physical and cognitive performance. The collection of human reaction times and supporting health related data was obtained from two groups comprising together 349 people of all ages - the visitors of the Days of Science and Technology 2016 held on the Pilsen central square and members of the Mensa Czech Republic visiting the neuroinformatics lab at the University of West Bohemia. Each provided dataset contains a complete or partial set of data obtained from the following measurements: hands and legs reaction times, color vision, spirometry, electrocardiography, blood pressure, blood glucose, body proportions and flexibility. It also provides a sufficient set of metadata (age, gender and summary of the participant's current life style and health) to allow researchers to perform further analysis. This article has two main aims. The first aim is to provide a well annotated collection of human reaction times and health related data that is suitable for further analysis of lifestyle and human cognitive and physical performance. This data collection is complemented with a preliminarily statistical evaluation. The second aim is to present a procedure of efficient acquisition of human reaction times and supporting health related data in non-lab and lab conditions.

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Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Collection of human reaction times and supporting health related data for analysis of cognitive and physical performance

et al. 2018

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“…A neuroinformatics laboratory specializing in the development of the software and hardware infrastructure (Mouček et al, 2014) for electrophysiology and in the analysis of EEG/ERP experiments was established at the University of West Bohemia under the Department of Computer Science and Engineering 2 in 2003. EEGBase (Ježek and Mouček, 2012), a portal for experimental data management and sharing, was proposed and developed within this infrastructure.…”

Section: Methodsmentioning

confidence: 99%

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

Papež

Mouček

2017

Front. Neuroinform.

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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.

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“…The NEMO project (Dou et al, 2007) provides an ontology, a set of tools and a centralized database for raw EEG and ERPs with an overall goal of better categorizing and labeling ERP features. The EEG/ERP portal (EEGBase) (Mouček et al, 2014) is a web-based system 7 that allows users to upload, download, and manage EEG/ERP data. The system provides some workflow and group management capabilities and focuses on the semantic enrichment of ERP data (Jezek et al, 2011).…”

Section: Materials and Toolsmentioning

confidence: 99%

Preparing Laboratory and Real-World EEG Data for Large-Scale Analysis: A Containerized Approach

Bigdely-Shamlo¹,

Makeig

Robbins

2016

Front. Neuroinform.

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Large-scale analysis of EEG and other physiological measures promises new insights into brain processes and more accurate and robust brain–computer interface models. However, the absence of standardized vocabularies for annotating events in a machine understandable manner, the welter of collection-specific data organizations, the difficulty in moving data across processing platforms, and the unavailability of agreed-upon standards for preprocessing have prevented large-scale analyses of EEG. Here we describe a “containerized” approach and freely available tools we have developed to facilitate the process of annotating, packaging, and preprocessing EEG data collections to enable data sharing, archiving, large-scale machine learning/data mining and (meta-)analysis. The EEG Study Schema (ESS) comprises three data “Levels,” each with its own XML-document schema and file/folder convention, plus a standardized (PREP) pipeline to move raw (Data Level 1) data to a basic preprocessed state (Data Level 2) suitable for application of a large class of EEG analysis methods. Researchers can ship a study as a single unit and operate on its data using a standardized interface. ESS does not require a central database and provides all the metadata data necessary to execute a wide variety of EEG processing pipelines. The primary focus of ESS is automated in-depth analysis and meta-analysis EEG studies. However, ESS can also encapsulate meta-information for the other modalities such as eye tracking, that are increasingly used in both laboratory and real-world neuroimaging. ESS schema and tools are freely available at www.eegstudy.org and a central catalog of over 850 GB of existing data in ESS format is available at studycatalog.org. These tools and resources are part of a larger effort to enable data sharing at sufficient scale for researchers to engage in truly large-scale EEG analysis and data mining (BigEEG.org).

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Software and hardware infrastructure for research in electrophysiology

Cited by 20 publications

References 32 publications

Collection of human reaction times and supporting health related data for analysis of cognitive and physical performance

Collection of human reaction times and supporting health related data for analysis of cognitive and physical performance

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

Preparing Laboratory and Real-World EEG Data for Large-Scale Analysis: A Containerized Approach

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