13 February 2018 Effect of filtration of signals of brain activity on quality of recognition of brain activity patterns using artificial intelligence methods
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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.
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Alexander E. Hramov, Nikita S. Frolov, Vyachaslav Yu. Musatov, "Effect of filtration of signals of brain activity on quality of recognition of brain activity patterns using artificial intelligence methods", Proc. SPIE 10493, Dynamics and Fluctuations in Biomedical Photonics XV, 104931D (13 February 2018); doi: 10.1117/12.2291675; https://doi.org/10.1117/12.2291675
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