title: Ð¡Ð¸ÑÑÐµÐ¼Ð° ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ ÑÐ¼Ð¾ÑÐ¸Ð¹ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ ÑÐ¸Ð³Ð½Ð°Ð»Ð° ÑÐ»ÐµÐºÑÑÐ¾ÑÐ½ÑÐµÑÐ°Ð»Ð¾Ð³ÑÐ°Ð¼Ð¼Ñ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ
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: Emotion recognition system based on electroencephalogram signal with using machine learning methods
Creators: name: ÐÐ»Ð°ÑÐµÐµÐ², ÐÐ»ÐµÐºÑÐµÐ¹
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: ÑÐ»ÐµÐºÑÑÐ¾ÑÐ½ÑÐµÑÐ°Ð»Ð¾Ð³ÑÐ°ÑÐ¸Ñ
Ð¸Ð½ÑÐµÑÑÐµÐ¹Ñ Ð¼Ð¾Ð·Ð³-ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑ
Ð¿ÑÐµÐ¾Ð±ÑÐ°Ð·Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð¤ÑÑÑÐµ
SVM
KNN
random forest
Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð½ÐµÐ¹ÑÐ¾Ð½
electroencephalography
brain-computer interface
Fourier transform
deep learning
neuron

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description: This research focuses on investigating various classification methods, data preprocessing techniques, feature extraction and selection approaches used in the development of emotion recognition systems based on electroencephalogram signals. The objectives of this study were as follows: 1) understand the principles and peculiarities involved in building an emotion recognition system. 2) review different methods for each key stage in constructing an emotion recognition system, including EEG data processing, and identifying the most effective techniques for emotion recognition. 3) develop a custom emotion recognition system using EEG signals. In this research, various emotion recognition systems were examined, and the effectiveness of the classification methods employed in these systems was compared. Additionally, a novel brain-computer interface system was developed to extract information about a persons emotional state from EEG signals, enabling interaction and further recognition of emotional states using different machine learning methods and system analysis techniques. The results of this work can be applied to all brain-computer interfaces utilizing EEG signals.