Cognitive computer graphics for information interpretation in real time intelligence systems

Nechaev, Yu. I.; Degtyarev, Anatoli; Boukhanovsky, Alexander V.

doi:10.1007/3-540-46043-8_69

Cited by 6 publications

(3 citation statements)

References 3 publications

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“…Visualization of the EEG signal in SciVi Although the visualization methods mentioned above are quite traditional, they are essential for conducting EEG-based experiments, so they should be included in any corresponding data processing pipeline. The SciVi visual analytics platform contains a lot of visual analytics tools [1,15] organized according to the principles of cognitive graphics [18]. In the future, we plan to adopt them for performing more complex visual analytics of EEG data.…”

Section: Visual Tools Supporting Eeg-based Bci Researchmentioning

confidence: 99%

Ontology-Driven Tools for EEG-Based Neurophysiological Research Automation

Ryabinin¹,

Чуприна²,

Labutin³

2021

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Section: Visual Tools Supporting Eeg-based Bci Researchmentioning

confidence: 99%

Ontology-Driven Tools for EEG-Based Neurophysiological Research Automation

Ryabinin¹,

Чуприна²,

Labutin³

2021

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“…Although the visualization methods mentioned above are quite traditional, they are essential for conducting EEG-based experiments, so they should be included in any corresponding data processing pipeline. The SciVi visual analytics platform contains a lot of visual analytics tools [1,15] organized according to the principles of cognitive graphics [18]. In the future, we plan to adopt them for performing more complex visual analytics of EEG data.…”

Section: Visual Tools Supporting Eeg-based Bci Researchmentioning

confidence: 99%

Ontology-Driven Toolset for Audio-Visual Stimuli Representation in EEG-Based BCI Research

Ryabinin

Чуприна

Labutin

2021

Proceedings of the 31th International Conference on Computer Graphics and Vision. Volume 2

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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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“…For the last 25 years the emergence of compute methods of cognitive visualization (graphics) have allowed to appear the enhanced perception capability of images by student and learning machines [6,7]. At the same time problems of intensification are being solved for presenting didactic material as well as for navigation and reasoning solutions (including the making cognitive maps, see Fig.…”

Section: B Psychological Componentmentioning

confidence: 99%

Automated Education: Tendency for Scientific Approaches Convergence

Uglev¹,

Sukhinin²

2014

Advances in Intelligent Systems Research

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-In this paper we discussing the possibility of the complex use of advances in area of pedagogic, psychology, neuroscience and informatics for the creation of teaching and learned systems of new generation. It is shown that the theoretical basis of automated education people (educational systems and programmathematical models) and machines (neural network simulation and intellectual robotic systems) can be integrated into a new scientific direction.

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Cognitive computer graphics for information interpretation in real time intelligence systems

Cited by 6 publications

References 3 publications

Ontology-Driven Tools for EEG-Based Neurophysiological Research Automation

Ontology-Driven Tools for EEG-Based Neurophysiological Research Automation

Ontology-Driven Toolset for Audio-Visual Stimuli Representation in EEG-Based BCI Research

Automated Education: Tendency for Scientific Approaches Convergence

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