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Proceedings Paper

Effect of filtration of signals of brain activity on quality of recognition of brain activity patterns using artificial intelligence methods
Author(s): Alexander E. Hramov; Nikita S. Frolov; Vyachaslav Yu. Musatov
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Paper Abstract

In present work we studied features of the human brain states classification, corresponding to the real movements of hands and legs. For this purpose we used supervised learning algorithm based on feed-forward artificial neural networks (ANNs) with error back-propagation along with the support vector machine (SVM) method. We compared the quality of operator movements classification by means of EEG signals obtained experimentally in the absence of preliminary processing and after filtration in different ranges up to 25 Hz. It was shown that low-frequency filtering of multichannel EEG data significantly improved accuracy of operator movements classification.

Paper Details

Date Published: 13 February 2018
PDF: 6 pages
Proc. SPIE 10493, Dynamics and Fluctuations in Biomedical Photonics XV, 104931D (13 February 2018); doi: 10.1117/12.2291675
Show Author Affiliations
Alexander E. Hramov, Yuri Gagarin State Technical Univ. of Saratov (Russian Federation)
Nikita S. Frolov, Yuri Gagarin State Technical Univ. of Saratov (Russian Federation)
Vyachaslav Yu. Musatov, Yuri Gagarin State Technical Univ. of Saratov (Russian Federation)


Published in SPIE Proceedings Vol. 10493:
Dynamics and Fluctuations in Biomedical Photonics XV
Valery V. Tuchin; Kirill V. Larin; Martin J. Leahy; Ruikang K. Wang, Editor(s)

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