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Proceedings Paper

Recognition and classification of oscillatory patterns of electric brain activity using artificial neural network approach
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Paper Abstract

In the paper we study the problem of recognition type of the observed object, depending on the generated pattern and the registered EEG data. EEG recorded at the time of displaying cube Necker characterizes appropriate state of brain activity. As an image we use bistable image Necker cube. Subject selects the type of cube and interpret it either as aleft cube or as the right cube. To solve the problem of recognition, we use artificial neural networks. In our paper to create a classifier we have considered a multilayer perceptron. We examine the structure of the artificial neural network and define cubes recognition accuracy.

Paper Details

Date Published: 3 March 2017
PDF: 6 pages
Proc. SPIE 10063, Dynamics and Fluctuations in Biomedical Photonics XIV, 1006317 (3 March 2017); doi: 10.1117/12.2250001
Show Author Affiliations
Svetlana V. Pchelintseva, Yuri Gagarin State Technical Univ. of Saratov (Russian Federation)
Anastasia E. Runnova, Yuri Gagarin State Technical Univ. of Saratov (Russian Federation)
Vyacheslav Yu. Musatov, Yuri Gagarin State Technical Univ. of Saratov (Russian Federation)
Alexander E. Hramov, Yuri Gagarin State Technical Univ. of Saratov (Russian Federation)
Saratov State Univ. (Russian Federation)


Published in SPIE Proceedings Vol. 10063:
Dynamics and Fluctuations in Biomedical Photonics XIV
Valery V. Tuchin; Kirill V. Larin; Martin J. Leahy; Ruikang K. Wang, Editor(s)

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