Ontology-Driven Tools for EEG-Based Neurophysiological Research Automation

Ryabinin, Konstantin; Чуприна, Светлана; Labutin, Ivan

doi:10.26583/sv.13.4.08

Cited by 3 publications

(7 citation statements)

References 14 publications

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“…Main focus points of this paper are devoted to tackling the following problems in order to improve the applicability of scientific visualization tools of SciVi developed by our team previously [1,2,3,4] in BCI studies to adapt them to the specific of the different IoT infrastructure, models and datasets:…”

Section: Key Contributionsmentioning

confidence: 99%

“…That pipeline was developed using the SciVi platform, an ontology-driven scientific visualization and visual analytics toolset [1]. Then we extended this pipeline to apply it for the task of audiovisual stimuli presentation for the neurophysiological studies [2,3]. However, there were two major technical issues with this pipeline implementation that we're going to address in this paper:…”

Section: Fig 1 a Common Scheme Of Neuroscience Experiments Involving ...mentioning

confidence: 99%

“…This separation also allows us to reuse "Montage Provider" node at the stage of configuring experimental setup. In our previous paper [2,3] we demonstrated a data monitoring pipeline (Fig. 4), but as of time of writing it had some drawbacks; in particular, visualization of the headsets' impedances was static in reference to electrode counts and their positions.…”

Section: Decoupling Headset Information From Signalmentioning

confidence: 99%

See 2 more Smart Citations

How to Improve Utilizing Neural Interface by Means of Ontology-Driven Scientific Visualization Tools

Чуприна¹,

Labutin²

2022

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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.

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Section: Key Contributionsmentioning

confidence: 99%

Section: Fig 1 a Common Scheme Of Neuroscience Experiments Involving ...mentioning

confidence: 99%

Section: Decoupling Headset Information From Signalmentioning

confidence: 99%

See 1 more Smart Citation

How to Improve Utilizing Neural Interface by Means of Ontology-Driven Scientific Visualization Tools

Чуприна¹,

Labutin²

2022

View full text Add to dashboard Cite

show abstract

“…Main focus points of this paper are devoted to tackling the following problems in order to improve the applicability of existing scientific visualization tools developed by our team previously [1,2,3,4] in BCI studies:…”

Section: Key Contributionsmentioning

confidence: 99%

“…That pipeline was based on the SciVi platform, an ontology-driven scientific visualization and visual analytics toolset [1]. Then we extended this pipeline to apply it for the task of audiovisual stimuli presentation for the neurophysiological studies [2,3]. However, there were two major technical issues with this pipeline that we're gonna address in this paper:…”

Section: Related Workmentioning

confidence: 99%

Scientific Visualization Tools to Improve Utilizing Neural Interface

Чуприна

Labutin

2022

Proceedings of the 32nd International Conference on Computer Graphics and Vision

Self Cite

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The technological progress in the field of Brain-Computer Interface and its integration with IoT put on the agenda the question of the fast transition of the technology from laboratory experiments into everyday life. But there are a lot of challenges and some of them, in particular, issues of replicability and reproducibility of experiments are under discussion in this paper. We also discuss how to improve utilizing neural Interface with the help of ontology-driven scientific visualization tools. 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.

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Generation of a Personalized Headcap 3D Model for Encephalography with Dry Electrodes

Chiruhin,

Ryabinin

2023

Proceedings of the 33rd International Conference on Computer Graphics and Vision

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The article presents a method for generating a 3D model of a collapsible headcap for dry electrodes of an electroencephalograph based on the parameters of a human head. A headcap for electrodes that precisely fits the head of a particular person is essential in the development of personalized human-machine brain-computer interfaces. A mathematical model is proposed, based on the approximation of the head shape using an ellipsoid and the placement of sockets for electrodes according to the international universal system 10-20. 3D bridges based on Bezier curves are used to connect sockets. The proposed method is implemented in the FreeCAD computer-aided design environment using the Python programming language. The headcap model has been generated. The accuracy of electrode placement on the headcap was verified using a 3D scan of a head obtained with a Calibry Mini scanner. The physical characteristics of the headcap are verified by printing it on a consumer-grade 3D printer. The resulting headcap is better suited for the person whose head parameters were taken than standard headcaps and requires less material and time for 3D printing.

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Ontology-Driven Tools for EEG-Based Neurophysiological Research Automation

Cited by 3 publications

References 14 publications

How to Improve Utilizing Neural Interface by Means of Ontology-Driven Scientific Visualization Tools

How to Improve Utilizing Neural Interface by Means of Ontology-Driven Scientific Visualization Tools

Scientific Visualization Tools to Improve Utilizing Neural Interface

Generation of a Personalized Headcap 3D Model for Encephalography with Dry Electrodes

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