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

BRŮHA, P., MOUČEK, R., VACEK, V., ŠNEJDAR, P., ČERNÁ, K., ŘEHOŘ, P. Collection of human reaction times and supporting health related data for analysis of cognitive and physical performance. Data in Brief, 2018, roč. 17, č. April 2018, s. 469-511. ISSN: 2352-3409
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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.

Modifications of unsupervised neural networks for single trial P300 detection

VAŘEKA, L., MAUTNER, P. Modifications of unsupervised neural networks for single trial P300 detection. Neural Network World, 2018, roč. 28, č. 1, s. 1-16. ISSN: 1210-0552
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P300 brain-computer interfaces (BCIs) have been gaining attention in recent years. To achieve good performance and accuracy, it is necessary to optimize both feature extraction and classification algorithms. This article aims at verifying whether supervised learning models based on self-organizing maps (SOM) or adaptive resonance theory (ART) can be useful for this task. For feature extraction, the state-of-the-art Windowed means paradigm was used. For classification, proposed classifiers were compared with state-of-the-art classifiers used in BCI research, such as Bayesian Linear Discriminant Analysis, or shrinkage LDA. Publicly available datasets from 15 healthy subjects were used for the experiments. The results indicated that SOM-based models yield better results than ART-based models. The best performance was achieved by the LASSO model that was comparable to state-of-the-art BCI classifiers. Further possibilities for improvements are discussed

BodyInNumbers - Software Prototype for Rapid Collection and Storage of Heterogeneous Health Related Data

BRŮHA, P., MOUČEK, R., ŠNEJDAR, P., VACEK, V., KRAFT, V., BOHMANN, D., VAŘEKA, L., ČERNÁ, K., ŘEHOŘ, P. BodyInNumbers - Software Prototype for Rapid Collection and Storage of Heterogeneous Health Related Data. 2017.
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BodyInNumbers is a software prototype for rapid collection, storage, management and visualization of heterogeneous health related data (reaction time data, P300 event-related component data, color vision, spirometry, electrocardiography, blood pressure, blood glucose, body proportions and flexibility) together with corresponding metadata (for example, a summary of the participant\'s current lifestyle and health). After data evaluation the user can view relevant information related to his/her health and fitness. The software was supported by the UWB grant SGS-2016-018 Data and Software Engineering for Advanced Applications, the project LO1506 of the Czech Ministry of Education, Youth and Sports under the program NPU I and the 2nd Internal grant scheme of UWB School of Computing, 2016. The project repository is available at https://gitlab.com/bodyinnumbers-public/bodyinnumbers-public.git. Information about the project is available at http://bodyinnumbers.kiv.zcu.cz/. The software prototype has been tested on 470 people in real environment (mainly during the Days of Science and Technology 2016 and 2017) and continuously improved according to operation difficulties. Published in: BRUHA, Petr, et al. Exercise and Wellness Health Strategy Framework. BIOSTEC 2017, 2017, 477.

Heart rate and sentiment experimental data with common timeline

SALAMON, J., MOUČEK, R. Heart rate and sentiment experimental data with common timeline. Data in Brief, 2017, roč. 15, č. December 2017, s. 851-861. ISSN: 2352-3409
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Sentiment extraction and analysis using spoken utterances or written corpora as well as collection and analysis of human heart rate data using sensors are commonly used techniques and methods. On the other hand, these have been not combined yet. The collected data can be used e.g. to investigate the mutual dependence of human physical and emotional activity. The paper describes the procedure of parallel acquisition of heart rate sensor data and tweets expressing sentiment and difficulties related to this procedure. The obtained datasets are described in detail and further discussed to provide as much information as possible for subsequent analyses and conclusions. Analyses and conclusions are not included in this paper. The presented experiment and provided datasets serve as the first basis for further studies where all four presented data sources can be used independently, combined in a reasonable way or used all together. For instance, when the data is used all together, performing studies comparing human sensor data, acquired noninvasively from the surface of the human body and considered as more objective, and human written data expressing the sentiment, which is at least partly cognitively interpreted and thus considered as more subjective, could be beneficial.

Archetype-based approach for modelling of electroencephalographic/event-related potentials data and metadata

PAPEŽ, V. Archetype-based approach for modelling of electroencephalographic/event-related potentials data and metadata. 1. vyd. Plzeň : neuveden, 2017, 170 s. ISBN: neuvedeno
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Currently, there is no common data standard in the experimental electro-encephalography/event. related potential (EEG/ERP) domain. Existing standardization efforts are mainly based on the conventional approaches and use generic data formats and containers (e.g. HDF5, odML) popular in the research community. This work draws on the medical/health characteristics of EEG/ERP data and investigates the feasibility of applying openEHR (an archetype-based approach for electronic health records representation) to modelling data stored in EEGBase, a portal for experimental EEG/ERP data management. The work 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 work are to (i) link existing EEGBase data/metadata and openEHR archetype structures; (ii) propose a new openEHR archetype set describing the EEG/ERP domain since this set of archetypes currently does not exist in public repositories. Apart from that, the work describes common data models (e.g. relational, object-oriented) and compares their expressive power in order to (i) determine the elements, which these models have in common; (ii) build a data model hierarchy according to their expressive power. The work uses the proposed archetypes and their reference models as semantic schemata to derive a specific data model for each level of the hierarchy. Finally, the work describes a~newly proposed personal electronic health records system for research purposes, which serves as a~first use-case of obtained results.

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