Systems and Means of Informatics
2021, Volume 31, Issue 2, pp 36-46
ELECTROENCEPHALOGRAPHY DATA ANALYSIS WITH CONVOLUTIONAL AND RECURRENT NEURAL NETWORKS
- I. A. Shanin
- S. A. Stupnikov
Abstract
Modern methods for the neurophysiological data analysis provide promising solutions to various problems both in the field of medical industry and in the field of brain-computer interfaces development. In this paper, a couple of important electroencephalography (EEG) data analysis problems are considered that are artifact detection and removal and human emotion recognition. Due to recent active development of algorithms based on deep artificial neural networks and to the cost reduction of commercial prototypes of brain-computer interfaces, the efficiency and robustness of modern methods for EEG data analysis is approaching a level sufficient for use outside the laboratory. The paper proposes methods for EEG data analysis based on convolutional and recurrent neural networks which make it possible to achieve high accuracy of artifact classification and emotion recognition over open data sets.
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[+] About this article
Title
ELECTROENCEPHALOGRAPHY DATA ANALYSIS WITH CONVOLUTIONAL AND RECURRENT NEURAL NETWORKS
Journal
Systems and Means of Informatics
Volume 31, Issue 2, pp 36-46
Cover Date
2021-05-20
DOI
10.14357/08696527210204
Print ISSN
0869-6527
Publisher
Institute of Informatics Problems, Russian Academy of Sciences
Additional Links
Key words
neurophysiology; neuroinformatics; electroencephalography; data analysis artificial neural networks; data artifact detection; emotion recognition
Authors
I. A. Shanin  and S. A. Stupnikov
Author Affiliations
Institute of Informatics Problems, Federal Research Center "Computer Science
and Control", Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation
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