ISSN 2079-3537      

 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                                                                                             

Scientific Visualization, 2020, volume 12, number 1, pages 1 - 9, DOI: 10.26583/sv.12.1.01

Visualization of EEG signal entropy in schizophrenia

Authors: I.E.  Kutepov1,A, A. V.  Krysko2,A, V.V.  Dobriyan3,A, T.V.  Yakovleva4,A, E.Yu.  Krylova5,A, V.A. Krysko6,A

Yuri Gagarin State Technical University of Saratov (SSTU)

1 ORCID: 0000-0002-1003-4496, iekutepov@gmail.com

2 ORCID: 0000-0002-9389-5602, anton.krysko@gmail.com

3 ORCID: 0000-0003-1136-1867, dobriy88@yandex.ru

4 ORCID: 0000-0003-3238-2317 , Yan-tan1987@mail.ru

5 ORCID: 0000-0002-7593-0320, Kat.Krylova@bk.ru

6 ORCID: 0000-0002-4914-764X, tak@san.ru

 

Abstract

This paper describes the visualization of the study of signal entropy in two groups of subjects. Brain activity signals were obtained using electroencephalogram (EEG). Two groups of adolescents – a schizophrenic group and a control group – were the subjects of the study. For each of the participants in both groups, 16 channels were recorded. Multi-scale entropy, model entropy, and approximated entropy were analyzed for signal complexity. The results of the entropic assessments were compared in the form of topographic images. Topographic images of the head surface were obtained based on a spherical spline. The activity of brain hemispheres for both groups was compared using the mean values of the cross-correlation function.

The study showed that the visualization of EEG signals could be a useful tool for classification of patients with schizophrenia and control groups. The analysis may be considered useful for the psychiatric examination of patients with schizophrenia.

On the other hand, the proposed approach is useful to extend the functionality of the educative robotics. Identification of schizophrenic subjects in the group of students provided by the robotic complex on the fly helps to avoid possible antisocial behavior while applying adequate training methods.

 

Keywords: EEG, schizophrenia, entropy, cross-correlation, data visualization, educational robotics.