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In the last decade, the recent advances in software and hardware facilitate the increase of interest in conducting experiments in the field of neurosciences, especially related to human-machine interaction. There are many mature and popular platforms leveraging experiments in this area including systems for representing the stimuli. However, these solutions often lack high-level adaptability to specific conditions, specific experiment setups, and third-party software and hardware, which may be involved in the experimental pipelines. This paper presents an adaptable solution based on ontology engineering that allows creating and tuning the EEG-based brain-computer interfaces. This solution relies on the ontology-driven SciVi visual analytics platform developed earlier. In the present work, we introduce new capabilities of SciVi, which enable organizing the pipeline for neuroscience-related experiments, including the representation of audio-visual stimuli, as well as retrieving, processing, and analyzing the EEG data. The distinctive feature of our approach is utilizing the ontological description of both the neural interface and processing tools used. This increases the semantic power of experiments, simplifies the reuse of pipeline parts between different experiments, and allows automatic distribution of data acquisition, storage, processing, and visualization on different computing nodes in the network to balance the computation load and to allow utilizing various hardware platforms, EEG devices, and stimuli controllers.

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The technological progress in the field of Brain-Computer Interface and its integration with IoT have now put on the agenda the question of the fast transition of the technology from laboratory experiments into everyday life. The paper presents an approach to improve utilizing neural interface with the help of ontology-driven scientific visualization tools taking into account the urgent problems of automatic adaptation to the specifics of different IoT infrastructure, models and datasets. Some issues of replicability and reproducibility of experiments are also under discussion in this paper. Using the principles of "clean-room reverse engineering" methodology to rewrite existing EEG device drivers we make it possible to embed visualization tools which dynamically render the streaming data coming from different EEG devices within a diverse IoT infrastructure without any legal complications.

Public speeches of the deputies of the Russian Federation State Duma represent productive empirical material that requires an interdisciplinary approach to its analysis. The goal of the reported study is defining the degree of the concord regarding the future of Russia among deputies and the localization of this agreement in the semantic space (equilibrium point). The empirical base under consideration comprises the transcripts of Russian Federation State Duma sittings. This dataset covers time period from 1994 to mid-2020 and includes 324 thousand phrases (27 million words) from 2773 deputies and other people. The paper presents the general design of the study of the State Duma deputies discourse using the "Semograph" information system, the SlovNet library for natural language text processing based on deep learning, the SciVi visual analytics platform, the map visualization module based on the Leaflet library, the geocoding of geographic objects based on the OpenStreetMap map provider and other tools. The application of the approach showed the importance of the institutional foundations of the symbolic deputies' mapping of the regions of the Volga Federal District (the electoral connection of a deputy with the region – deputies elected by party list or pluralist rule).