title: ÐÑÑÐµÐ½ÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ Ð¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°ÑÐµÐ»ÐµÐ¹ Ð¿Ð¾ Ð­Ð­Ð Ð³Ð¾Ð»Ð¾Ð²Ð½Ð¾Ð³Ð¾ Ð¼Ð¾Ð·Ð³Ð° Ð¿ÑÐ¸ Ð¼Ð¾ÑÐ³Ð°Ð½Ð¸Ð¸ 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: Autentification of users by EEG of the brain then blinking using machine learning methods
Creators: name: ÐÐµÐ»ÐµÐ½ÐºÐ¾, ÐÐ°Ð²ÐµÐ»
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: 2021
Subjects: Ð°ÑÑÐµÐ½ÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ
Ð¼Ð¾ÑÐ³Ð°Ð½Ð¸Ñ
Ð­Ð­Ð
Neuroplay-8Cap
Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Random Forest
k Nearest Neighbors
Support Vectors Classifier
authentication
blinking
EEG
machine Learning

description: Ð ÑÑÐ°ÑÑÐµ Ð¿ÑÐ¸Ð²ÐµÐ´ÐµÐ½Ñ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð² Ð¾Ð±Ð»Ð°ÑÑÐ¸ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ ÑÐ»ÐµÐºÑÑÐ¾ÑÐ½ÑÐµÑÐ°Ð»Ð¾Ð³ÑÐ°ÑÐ¸Ð¸ (Ð­Ð­Ð) Ð² ÐºÐ°ÑÐµÑÑÐ²Ðµ Ð¼ÐµÑÐ¾Ð´Ð° Ð±Ð¸Ð¾Ð¼ÐµÑÑÐ¸ÑÐµÑÐºÐ¾Ð¹ Ð°ÑÑÐµÐ½ÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ ÑÐµÐ»Ð¾Ð²ÐµÐºÐ°. Ð ÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½Ñ Ð´Ð²Ð° Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Ð°ÑÑÐµÐ½ÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸, Ð¾ÑÐ½Ð¾Ð²Ð°Ð½Ð½ÑÐµ Ð½Ð° Ð·Ð°Ð¿Ð¸ÑÑÑ Ð­Ð­Ð Ð²ÑÐ½ÑÐ¶Ð´ÐµÐ½Ð½ÑÑ Ð¼Ð¾ÑÐ³Ð°Ð½Ð¸Ð¹. ÐÐ»Ñ ÑÐ±Ð¾ÑÐ° Ð´Ð°Ð½Ð½ÑÑ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÐµÑÑÑ ÑÐ»ÐµÐºÑÑÐ¾ÑÐ½ÑÐµÑÐ°Ð»Ð¾Ð³ÑÐ°Ñ NeuroPlay-8Cap, Ð·Ð°Ð¿Ð¸ÑÑÐ²Ð°ÑÑÐ¸Ð¹ ÑÐ¸Ð³Ð½Ð°Ð» Ñ ÑÐ°ÑÑÐ¾ÑÐ¾Ð¹ Ð´Ð¸ÑÐºÑÐµÑÐ¸Ð·Ð°ÑÐ¸Ð¸ 125 ÐÑ. ÐÑÐµÐ´Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ° Ð´Ð°Ð½Ð½ÑÑ Ð¾ÑÐ½Ð¾Ð²Ð°Ð½Ð° Ð½Ð° Ð²ÐµÐ¹Ð²Ð»ÐµÑ-Ð¿ÑÐµÐ¾Ð±ÑÐ°Ð·Ð¾Ð²Ð°Ð½Ð¸Ð¸ ÐÐ¾Ð±ÐµÑÐ¸ 2-Ð³Ð¾ Ð¿Ð¾ÑÑÐ´ÐºÐ° (DWT DB2) Ð¸ Ð½Ð° ÑÐ°ÑÐ¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ð¸ ÑÐ¿ÐµÐºÑÑÐ°Ð»ÑÐ½Ð¾Ð¹ Ð¿Ð»Ð¾ÑÐ½Ð¾ÑÑÐ¸ Ð¼Ð¾ÑÐ½Ð¾ÑÑÐ¸ Ð°Ð½Ð°Ð»Ð¸Ð·Ð¸ÑÑÐµÐ¼Ð¾Ð³Ð¾ ÑÐ¸Ð³Ð½Ð°Ð»Ð°, Ð½Ð°ÑÐ¾Ð´Ð¸Ð¼Ð¾Ð¹ Ð¼ÐµÑÐ¾Ð´Ð¾Ð¼ Ð£ÑÐ»ÑÐ° (PSD, Welch Method). Ð Ð°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ðµ Ð²ÐµÐ´ÑÑÑÑ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¾ÑÐ¾Ð² Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð¼ÐµÑÐ¾Ð´Ð° Ð¾Ð¿Ð¾ÑÐ½ÑÑ Ð²ÐµÐºÑÐ¾ÑÐ¾Ð² (Support Vectors Classifier), ÑÐ»ÑÑÐ°Ð¹Ð½Ð¾Ð³Ð¾ Ð»ÐµÑÐ° (Random Forest Classifier) Ð¸ k Ð±Ð»Ð¸Ð¶Ð°Ð¹ÑÐ¸Ñ ÑÐ¾ÑÐµÐ´ÐµÐ¹ (k Nearest Neighbors Classifier). ÐÐ±Ðµ Ð²Ð°ÑÐ¸Ð°ÑÐ¸Ð¸ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Ð´Ð°ÑÑ ÑÐ¾ÑÐ¾ÑÐ¸Ð¹ Ð¿Ð¾ÐºÐ°Ð·Ð°ÑÐµÐ»Ñ ÑÐ¾ÑÐ½Ð¾ÑÑÐ¸ Ð² 90â95% Ð² Ð·Ð°Ð²Ð¸ÑÐ¸Ð¼Ð¾ÑÑÐ¸ Ð¾Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÐµÐ¼Ð¾Ð³Ð¾ Ð¼ÐµÑÐ¾Ð´Ð° ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸.
description: The article presents the results of a study on the use of electroencephalography (EEG) as a method of biometrical authentication of a person. Implemented two authentication algorithms based on EEG recordings of forced blinks. For data collection, the NeuroPlay-8Cap electroencephalograph is used, which records a signal with a sampling frequency of 125 Hz. Data preprocessing is based on the 2nd-order Daubechies wavelet transform (DWT DB2) and on the distribution of the spectral power density of the analyzed signal (PSD), found by the Welsh method. Recognition is performed using classifiers based on the Support Vectors Classifier (SVC), Random Forest (RF), and k Nearest Neighbors (kNN). Both variations of the algorithm give an accuracy rate of 90-95%, depending on the classification method used.