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

Svetlana V. Pchelintseva; Anastasia E. Runnova; Vyacheslav Yu. Musatov; Alexander E. Hramov

doi:10.1117/12.2250001

3 March 2017 Recognition and classification of oscillatory patterns of electric brain activity using artificial neural network approach

Svetlana V. Pchelintseva, Anastasia E. Runnova, Vyacheslav Yu. Musatov, Alexander E. Hramov

Author Affiliations +

Proceedings Volume 10063, Dynamics and Fluctuations in Biomedical Photonics XIV; 1006317 (2017) https://doi.org/10.1117/12.2250001
Event: SPIE BiOS, 2017, San Francisco, California, United States

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.

Citation Download Citation

Svetlana V. Pchelintseva, Anastasia E. Runnova, Vyacheslav Yu. Musatov, and Alexander E. Hramov "Recognition and classification of oscillatory patterns of electric brain activity using artificial neural network approach", Proc. SPIE 10063, Dynamics and Fluctuations in Biomedical Photonics XIV, 1006317 (3 March 2017); https://doi.org/10.1117/12.2250001

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