Open AccessSEPARATION OF ELECTROENCEPHALOGRAM LOW-FREQUENCY COMPONENTS ON THE BASIS OF THE STOCHASTIC RESONANCE EFFECT
Author(s) -
Oksana Kharchenko,
Yu.F. Lonin,
Л.П. Забродіна,
В. М. Карташов
Publication year - 2021
Publication title -
problems of atomic science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.216
H-Index - 17
eISSN - 1562-6016
pISSN - 1682-9344
DOI - 10.46813/2021-134-135
Subject(s) - electroencephalography , neuropathology , computation , autocorrelation , resonance (particle physics) , basis (linear algebra) , computer science , stochastic resonance , nuclear magnetic resonance , physics , algorithm , mathematics , artificial intelligence , statistics , noise (video) , neuroscience , disease , medicine , psychology , atomic physics , geometry , pathology , image (mathematics)
The paper describes the method for electroencephalogram (EEG) analysis based on the stochastic resonance (SR) effect. The numerical computation has provided the separation of low frequency components that fall within the δ-rhythm band. This is currently central in the neuropathology diagnostics, because the presence of low frequencies in the EEG is abnormal and bears witness to the disease. For verification, the data obtained with the use of the SR effect have been compared with the computations based on the autocorrelation function (ACF) processing. The comparison has shown their good agreement.