Adaptive Recurrent Neural Network For Reduction Of Noise And Estimation Of Source From Recorded Eeg Signals

Pardede, Jasman and Turnip, Mardi and Manalu, Darwis Robinson and Turnip, Arjon (2015) Adaptive Recurrent Neural Network For Reduction Of Noise And Estimation Of Source From Recorded Eeg Signals. ARPN Journal of Engineering and Applied Sciences, 10 (3). ISSN 1819-6608 (In Press)

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Abstract

In recording the EEG signals are often contaminated by a large of signals called artifacts such that the brain activity (source) difficult to estimate. There are different kinds of artifacts such as power line noise, electromyogram, electrocardiogram and electrooculogram. In this research, an adaptive recurrent neural network (ARNN) for estimation of source and reduction of noise from recorded EEG signals is proposed. In the experiment, the EEG signals are recorded on three conditions, which is normal conditions, closed eyes, and blinked eyes. After processing, the dominant frequency of the EEG signal is obtained in the range of 12-14 Hz either on normal conditions, closed eyes, and blinked eyes. The experimental results show that the ARNN method w

Item Type: Article
Subjects: T Technology > T Technology (General)
Divisions: Karya Tulis Ilmiah
Depositing User: Asep Kamaludin
Date Deposited: 11 May 2018 02:39
Last Modified: 11 May 2018 02:39
URI: http://eprints.itenas.ac.id/id/eprint/79

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