Discrete and Continuous Models and Applied Computational ScienceDiscrete and Continuous Models and Applied Computational ScienceDiscrete and Continuous Models and Applied Computational Science2658-46702658-7149Peoples' Friendship University of Russia named after Patrice Lumumba (RUDN University)3592310.22363/2658-4670-2023-31-3-273-281KPCBBQArticlesСтатьиResearch ArticleBrain-computer interaction modeling based on the stable diffusion modelМоделирование взаимодействия «мозг - компьютер» на основе модели стабильной диффузииhttps://orcid.org/0000-0003-3651-7629ShchetininEugeny Yu.ЩетининЕ. Ю.

Doctor of Physical and Mathematical Sciences, Lecturer of Department of Mathematics

riviera-molto@mail.ruFinancial University under the Government of the Russian FederationФинансовый университет при Правительстве Российской Федерации12092023313VOL 31, NO3 (2023)ТОМ 31, №3 (2023)27328112092023Copyright ©; 2023, Shchetinin E.Y.Copyright ©; 2023, Щетинин Е.Ю.2023Shchetinin E.Y.Щетинин Е.Ю.https://creativecommons.org/licenses/by-nc/4.0https://journals.rudn.ru/miph/article/view/35923

This paper investigates neurotechnologies for developing brain-computer interaction (BCI) based on the generative deep learning Stable Diffusion model. An algorithm for modeling BCI is proposed and its training and testing on artificial data is described. The results are encouraging researchers and can be used in various areas of BCI, such as distance learning, remote medicine and the creation of robotic humanoids, etc.

В этой статье исследуются нейротехнологии для развития взаимодействия «мозг - компьютер» (BCI) на основе генеративной модели стабильной диффузии с глубоким обучением. Предложен алгоритм моделирования BCI и описано его обучение и тестирование на искусственных данных. Полученные результаты обнадёживают исследователей и могут быть использованы в различных областях BCI, таких как дистанционное обучение, удалённая медицина, создание роботов-гуманоидов и т.д.

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