title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¼Ð½Ð¾Ð³Ð¾Ð¼ÐµÑÐ½ÑÑ Ð¾Ð±ÑÐµÐºÑÐ¾Ð² Ð´Ð»Ñ Ð¸Ð½ÑÐµÑÑÐµÐ¹ÑÐ° Ð¼Ð¾Ð·Ð³-ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Development of an algorithm for the classification of multidimensional objects for the brain-computer interface
Creators: name: ÐÐ¾Ð»ÐµÑÐ¾Ð², Ð¡ÐµÑÐ³ÐµÐ¹
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2023
Subjects: Ð¸Ð½ÑÐµÑÑÐµÐ¹Ñ Ð¼Ð¾Ð·Ð³-ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑ (ÐÐÐ)
ÑÐ»ÐµÐºÑÑÐ¾ÑÐ½ÑÐµÑÐ°Ð»Ð¾Ð³ÑÐ°Ð¼Ð¼Ð° (Ð­Ð­Ð)
ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ ÑÐ¸Ð³Ð½Ð°Ð»Ð°
Ð¾Ð±ÑÐ°Ð·Ñ Ð¼Ð¾ÑÐ¾ÑÐ½Ð¾Ð¹ Ð°ÐºÑÐ¸Ð²Ð½Ð¾ÑÑÐ¸
Ð°Ð½Ð°Ð»Ð¸Ð· Ð­Ð­Ð
brain-computer interface (BCI)
electroencephalogram (EEG)
signal classification
motor activity patterns
EEG analysis

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description: The subject of the graduate qualification work is "Development of an algorithm for the classification of multidimensional objects for the brain-computer interface." The object of the study is the EEG signal and the methods used to detect patterns of motor activity based on this signal, which are currently used in BCIs. The purpose of the work is to develop an algorithm for analyzing patterns of motor activity based on the EEG signal for the implementation of BCI with a high-accuracy classifier. In the course of the work, experimental studies of methods for analyzing the EEG signal for detecting patterns of motor activity were carried out. The results of the work showed that the existing methods of EEG analysis used in BCI detect events with sufficient accuracy. However, depending on the implementation of the BCI stages, their effectiveness varies. In a practical test of existing methods, the most effective classification method turned out to be the support vector machine (SVM). However, due to the time of its operation, it is not suitable for BCI implementations. Therefore, an own EEG analysis algorithm was developed for detecting motor activity patterns, with a classifier of increased accuracy, based on the functions of the epoch belonging to events. An experimental study under simulated conditions showed that it is possible to achieve a detection accuracy of 98.5% of events, with an average interface delay of 230 ms.