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Framework for Automatic Generation of Graphical Layout Compatible with Multiple Platforms

JEŽEK, P., MOUČEK, R., FRANC, Y.L., WACHTLER, T., GREWE, J. Framework for Automatic Generation of Graphical Layout Compatible with Multiple Platforms. In VL/HCC 2013. Piscataway: IEEE, 2013. s. 193-194. ISBN: 978-1-4799-0369-6
Abstract PDF BibTeX

Most data management systems include a database in the backend to store data and the associated metadata and a web-based user interface to access and modify the data/metadata. User interfaces are specifically tailored for representing a unique database structure and cannot be easily reused for other database structure. Furthermore the generated web-based layouts are often not compatible for other platform such as desktop applications or mobile devices. We are proposing here a general framework for designing a graphical layout compatible with different platforms including mobile devices and independent of the database structure. This framework is based on a model-driven approach using annotations of database entities that will be used to create desired layouts. A use case study is presented on a database designed for neuroscience experiments.

Single Channel Eye-Blinking Artifacts Detections

VAŘEKA, L., BRŮHA, P., MOUČEK, R. Single Channel Eye-Blinking Artifacts Detections. In 2013 International Conference on Applied Electronics. Plzeň: ZČU v Plzni, 2013. s. 313-316. ISBN: 978-80-261-0166-6 , ISSN: 1803-7232
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For neurophysiological data sharing, it is vital for researchers to validate their data from different perspectives. Detection of eye artifacts is especially important since the artifacts may distort the data to an unacceptable extent. Most methods for their correction either require EOG channels or they are very time consuming. This paper proposes a fast method that does not require EOG channels. It outperforms amplitudebased methods in accuracy and Independent Component Analysis in computational complexity.

The Ontology for Experimental Neurophysiology: a first step toward semantic annotations of neurophysiology data and metadata

BRŮHA, P., PAPEŽ, V., BANDROWSKI, A., GREWE, J., MOUČEK, R., TRIPATHY, S., WACHTLER, T., FRANC, Y. L. The Ontology for Experimental Neurophysiology: a first step toward semantic annotations of neurophysiology data and metadata. Stockholm, Sweden, 2013.
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Several projects in the Neuroinformatics community are currently developed or used to store electrophysiology data and metadata. Semantic web technologies allow federating the different web-based resources around a common semantic model, namely an ontology. Analysis of existing ontological resources reveals a lack of terms for accurately and unambiguously annotating electrophysiological data and metadata. With the development of different resources for describing and sharing this particular type of data, the community needs controlled vocabularies to describe the different types of electrophysiology recording paradigms. The goal of Ontology for Experimental Neurophysiology (OEN) is to propose such a unique controlled vocabulary, relying on existing ontologies, which will allow the annotation of these emerging resources and provide a framework for enhancing interoperability . To build such vocabulary, or ontology, we created a dedicated workgroup involving relevant initiatives such as the EEGBase (eegdatabase.kiv.zcu.cz/home.html), the G-Node (www.g-node.org), the INCF task force on standards for sharing of electrophysiology data (www.incf.org/programs/datasharing/electrophysiology-task-force), NIF (www.neuinfo.org) and Neuroelectro.org (www.neurolectro.org).

Considerations for developing a standard for storing electrophysiology data in HDF5

MOUČEK, R. Considerations for developing a standard for storing electrophysiology data in HDF5. Stockholm, 2013.
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The INCF Program on Standards for Data Sharing has a working group that is developing a standard for storing electrophysiology data in HDF5. The most important requirement of such a standard is to accommodate the common types of data used in electrophysiology and also the metadata required to describe them. Neuroshare defines four data types: analog signals, segments, neural events and experimental events; as well as some metadata. A standard needs to efficiently store these data types, and probably also imaging data and some kinds of data generated in the data processing chain. Further, a standard way of storing the metadata must be specified. The set of metadata required to describe electrophysiology data is difficult to determine a priori because the types of experiments are so varied. So, a flexible mechanism must be used which allows referencing and specifying values for currently existing ontologies and also accommodates information not currently systematized. Techniques to include post-experiment annotations of data, and for relating different data parts, are also required. So far, the working group entertains two approaches towards defining a standard, which may eventually be merged. One, currently named Pandora, defines a generic data model that can be used with HDF5 or other storage back-ends. Due to the generic nature, the data model can be used to store various kinds of neuroscience data. The other proposal, called epHDF, defines domain specific schemata for storing electrophysiology data in HDF5. For any approach, a suite of test data sets to help evaluate a proposed standard is needed, and tools to allow validating data files are desirable.

EEG/ERP Portal for Android Platform

JEŽEK, P., MOUČEK, R. EEG/ERP Portal for Android Platform. Stockholm, 2013.
Abstract PDF BibTeX

Lack of tools for experimental data/metadata management has been solved by developing the EEG/ERP Portal. The advantage of the EEG/ERP Portal is its availability from every computer connected to the Internet. Such solution is sufficient for collecting the most of experiments performed in the laboratory. On the other hand, situations when using a computer is not a viable option are frequent. Many experiments are conducted outside the laboratory with a portable measuring device. Facing mentioned needs we have developed a system for collecting experimental data/metadata running on mobile devices. The system contains a set of forms where a user can fill metadata describing an experiment. The set of metadata is equivalent to metadata that the user can fill in the EEG/ERP Portal. The metadata are described by the internal portal ontology. In addition, the user can upload binary data. The communication of both the mobile EEG/ERP Portal and web EEG/ERP Portal is ensured using RESTfull web services. Server-client architecture is used. A server part is implemented in the EEG/ERP Portal. The server provides access to the database and sends data to the client implemented inside the mobile device. The communication between the server and client is secured using SSL protocol. User credentials are required; the EEG/ERP Portal user account is used to verify the client.

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