Thesis Details
Identifikace a verifikace osob s využitím EEG
The aim of this work was to create a brain-computer interface that reliably identifies and verifies a person using his electroencephalographic signals. Creating a user profile and verifying it is based on processing reactions to his own face, and the face of strangers or acquaintances. Algorithms such as bandpass and noise removal using wavelet transformation are user to filter signals. The classification of reactions is performed using a convolutional neural network or linear discriminant analysis. The average accuracy of the linear discriminant analysis is 66.2 % and of the convolutional neural network is 58.7 %. The maximum achieved accuracy was with linear discriminant analysis and at 93.7 %.
brain activity, electroencephalography, EEG, brain-computer interface, BCI, identification, verification, signal filtering, bandpass, wavelet transformation, classification, self face, non-self face, P300, N170, N250, ERP, artifacts, neural network, linear discriminant analysis, CNN, LDA
Beran Vítězslav, doc. Ing., Ph.D. (DCGM FIT BUT), člen
Burget Radek, doc. Ing., Ph.D. (DIFS FIT BUT), člen
Lengál Ondřej, Ing., Ph.D. (DITS FIT BUT), člen
@bachelorsthesis{FITBT23540,
author = "Roland \v{Z}itn\'{y}",
type = "Bachelor's thesis",
title = "Identifikace a verifikace osob s vyu\v{z}it\'{i}m EEG",
school = "Brno University of Technology, Faculty of Information Technology",
year = 2021,
location = "Brno, CZ",
language = "slovak",
url = "https://www.fit.vut.cz/study/thesis/23540/"
}