Научная визуализация, 2022, том 14, номер 4, страницы 83 - 96, DOI: 10.26583/sv.14.4.08
How to Improve Utilizing Neural Interface by Means of Ontology-Driven Scientific Visualization Tools
Авторы: S.I. Chuprina1, I.A. Labutin2
Perm State University
1 ORCID: 0000-0002-2103-3771, chuprinas@inbox.ru
2 ORCID: 0000-0001-6858-1479, i.a.labutin@yandex.ru
Аннотация
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.
Ключевые слова: Internet of Things, Ontology Engineering, Brain-Computer Interface, Clean-Room Reverse Engineering, Replicability, Reproducibility.
