Souhrn: Propojení těla a mysli a jejich vzájemná konjunkce jsou stále aktuálnějším, mnohooborovým tématem. Cíl: Cílem práce bylo zjistit, do jaké míry působí naše myšlenky v profesionálním tanečním prostředí na průběh mozkových frekvencí a jakým způsobem se propisují do našeho organizmu a pohybu. Metody: V rámci studie proběhlo měření mozkové činnosti nositelným EEG zařízením Muse a vizuální analýza pohybu z tanečního prostředí, prvku plié (flexe a extenze kolenních kloubů). Sledovanými a měřenými jedinci bylo 16 studentů Katedry tance, Hudební a taneční fakulty Akademie múzických umění v Praze. Sledovali jsme mozkové frekvence v mnoha fázích (situacích): bez konkrétní myšlenky (soustředění se) a bez pohybu, při konkrétní myšlence bez pohybu, pohyb bez konkrétní myšlenky, pohyb s konkrétní myšlenkou a na závěr opět myšlenku bez pohybu, která byla mimo obor a rámec výzkumu. Výsledky: U 14 účastníků experimentu jsme získali dostatečně kvalitní EEG signál k další analýze. U 12 účastníků byla prokázána změna v mozkových frekvencích odlišujících aktivity s nižší a vyšší vědomou koncentrací. Závěr: Výstup popisuje zjištění ohledně propojení myšlenek, těla a pohybu na základě měřitelných dat. EEG zařízení Muse je použitelné pro získávání EEG signálu dostatečné kvality v experimentech vyžadujících pohyb odehrávající se v reálných podmínkách. V získaném EEG signálu je možné detekovat změny v mozkové aktivitě účastníků při stavech uvolněné bdělosti a vědomé koncentrace. Vizuální analýza pohybu reflektuje, že syntéza specifické myšlenky a pohybu přispívá k eliminaci špatných pohybových návyků, které ruší nejen technickou čistotu provedení pohybového prvku, ale také optimální držení těla, koordinaci pohybu a jednotlivých anatomických struktur.
Summary: The connection of body and mind and their mutual conjunction is an increasingly topical topic in many fields. Objective: The aim of the work was to find out to what extent our thoughts in a professional dance environment affect the course of brain frequencies and how they are prescribed in our organism and movement. Methods: As part of the study, brain activity was measured with the Muse wearable EEG device and visual analysis of movement from the dance environment, the plié element (flexion and extension of the knee joints). The monitored and measured individuals were 16 students of the Dance Department of Music and Dance Faculty of the Music Academy of Performing Arts in Prague. We monitored brain frequencies in many phases (situations): without a specific thought (concentration) and without movement, with a specific thought without movement, movement without a specific thought, movement with a specific thought, and finally again a thought without movement, which was out of scope and research framework. Results: For 14 participants in the experiment, we obtained an EEG signal of sufficient quality so that it could be further analyzed. A change in brain frequencies distinguishing activities with lower and higher conscious concentration was demonstrated in 12 participants. Conclusion: The output describes the findings regarding the connection of thoughts, body, and movement based on measurable data. The Muse EEG device is useful for obtaining an EEG signal of sufficient quality in experiments requiring movement and taking place in real conditions. In the obtained EEG signal, it is possible to detect changes in the participant‘s brain activity during states of relaxed alertness and conscious concentration. Visual analysis of movement reflects that the synthesis of a specific thought and movement contributes to eliminating bad habits, which destroy not only the technical purity of the performance of the movement element but also optimal posture, coordination of movement, and individual anatomical structures.
- MeSH
- elektroencefalografie metody MeSH
- lidé MeSH
- mapování mozku * MeSH
- pohyb fyziologie MeSH
- pozornost MeSH
- tanec MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- klinická studie MeSH
Poor lifestyle leads potentially to chronic diseases and low-grade physical and mental fitness. However, ahead of time, we can measure and analyze multiple aspects of physical and mental health, such as body parameters, health risk factors, degrees of motivation, and the overall willingness to change the current lifestyle. In conjunction with data representing human brain activity, we can obtain and identify human health problems resulting from a long-term lifestyle more precisely and, where appropriate, improve the quality and length of human life. Currently, brain and physical health-related data are not commonly collected and evaluated together. However, doing that is supposed to be an interesting and viable concept, especially when followed by a more detailed definition and description of their whole processing lifecycle. Moreover, when best practices are used to store, annotate, analyze, and evaluate such data collections, the necessary infrastructure development and more intense cooperation among scientific teams and laboratories are facilitated. This approach also improves the reproducibility of experimental work. As a result, large collections of physical and brain health-related data could provide a robust basis for better interpretation of a person's overall health. This work aims to overview and reflect some best practices used within global communities to ensure the reproducibility of experiments, collected datasets and related workflows. These best practices concern, e.g., data lifecycle models, FAIR principles, and definitions and implementations of terminologies and ontologies. Then, an example of how an automated workflow system could be created to support the collection, annotation, storage, analysis, and publication of findings is shown. The Body in Numbers pilot system, also utilizing software engineering best practices, was developed to implement the concept of such an automated workflow system. It is unique just due to the combination of the processing and evaluation of physical and brain (electrophysiological) data. Its implementation is explored in greater detail, and opportunities to use the gained findings and results throughout various application domains are discussed.
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
There is great need for coordination around standards and best practices in neuroscience to support efforts to make neuroscience a data-centric discipline. Major brain initiatives launched around the world are poised to generate huge stores of neuroscience data. At the same time, neuroscience, like many domains in biomedicine, is confronting the issues of transparency, rigor, and reproducibility. Widely used, validated standards and best practices are key to addressing the challenges in both big and small data science, as they are essential for integrating diverse data and for developing a robust, effective, and sustainable infrastructure to support open and reproducible neuroscience. However, developing community standards and gaining their adoption is difficult. The current landscape is characterized both by a lack of robust, validated standards and a plethora of overlapping, underdeveloped, untested and underutilized standards and best practices. The International Neuroinformatics Coordinating Facility (INCF), an independent organization dedicated to promoting data sharing through the coordination of infrastructure and standards, has recently implemented a formal procedure for evaluating and endorsing community standards and best practices in support of the FAIR principles. By formally serving as a standards organization dedicated to open and FAIR neuroscience, INCF helps evaluate, promulgate, and coordinate standards and best practices across neuroscience. Here, we provide an overview of the process and discuss how neuroscience can benefit from having a dedicated standards body.
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.
- Publikační typ
- časopisecké články MeSH
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.
- Publikační typ
- časopisecké články MeSH
Background: Developmental coordination disorder (DCD) is described as a motor skill disorder characterized by a marked impairment in the development of motor coordination abilities that significantly interferes with performance of daily activities and/or academic achievement. Since some electrophysiological studies suggest differences between children with/without motor development problems, we prepared an experimental protocol and performed electrophysiological experiments with the aim of making a step toward a possible diagnosis of this disorder using the event-related potentials (ERP) technique. The second aim is to properly annotate the obtained raw data with relevant metadata and promote their long-term sustainability. Results: The data from 32 school children (16 with possible DCD and 16 in the control group) were collected. Each dataset contains raw electroencephalography (EEG) data in the BrainVision format and provides sufficient metadata (such as age, gender, results of the motor test, and hearing thresholds) to allow other researchers to perform analysis. For each experiment, the percentage of ERP trials damaged by blinking artifacts was estimated. Furthermore, ERP trials were averaged across different participants and conditions, and the resulting plots are included in the manuscript. This should help researchers to estimate the usability of individual datasets for analysis. Conclusions: The aim of the whole project is to find out if it is possible to make any conclusions about DCD from EEG data obtained. For the purpose of further analysis, the data were collected and annotated respecting the current outcomes of the International Neuroinformatics Coordinating Facility Program on Standards for Data Sharing, the Task Force on Electrophysiology, and the group developing the Ontology for Experimental Neurophysiology. The data with metadata are stored in the EEG/ERP Portal.
- MeSH
- akustická stimulace MeSH
- datové kurátorství MeSH
- dítě MeSH
- elektroencefalografie MeSH
- evokované potenciály MeSH
- komorbidita MeSH
- kvantitativní znak dědičný MeSH
- lidé MeSH
- počítačová simulace MeSH
- poruchy motorických dovedností diagnóza MeSH
- reakční čas MeSH
- reprodukovatelnost výsledků MeSH
- software MeSH
- světelná stimulace MeSH
- Check Tag
- dítě MeSH
- lidé MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- Publikační typ
- abstrakt z konference MeSH
The article deals with and discusses two main approaches in building semantic structures for electrophysiological metadata. It is the use of conventional data structures, repositories, and programming languages on one hand and the use of formal representations of ontologies, known from knowledge representation, such as description logics or semantic web languages on the other hand. Although knowledge engineering offers languages supporting richer semantic means of expression and technological advanced approaches, conventional data structures and repositories are still popular among developers, administrators and users because of their simplicity, overall intelligibility, and lower demands on technical equipment. The choice of conventional data resources and repositories, however, raises the question of how and where to add semantics that cannot be naturally expressed using them. As one of the possible solutions, this semantics can be added into the structures of the programming language that accesses and processes the underlying data. To support this idea we introduced a software prototype that enables its users to add semantically richer expressions into a Java object-oriented code. This approach does not burden users with additional demands on programming environment since reflective Java annotations were used as an entry for these expressions. Moreover, additional semantics need not to be written by the programmer directly to the code, but it can be collected from non-programmers using a graphic user interface. The mapping that allows the transformation of the semantically enriched Java code into the Semantic Web language OWL was proposed and implemented in a library named the Semantic Framework. This approach was validated by the integration of the Semantic Framework in the EEG/ERP Portal and by the subsequent registration of the EEG/ERP Portal in the Neuroscience Information Framework.
- Publikační typ
- časopisecké články MeSH
- MeSH
- akustická stimulace MeSH
- analýza rozptylu MeSH
- dítě MeSH
- elektroencefalografie statistika a číselné údaje MeSH
- lidé MeSH
- poruchy motorických dovedností * patofyziologie MeSH
- sluchová percepce * MeSH
- sluchové evokované potenciály * fyziologie MeSH
- Check Tag
- dítě MeSH
- lidé MeSH
- Publikační typ
- práce podpořená grantem MeSH
