DefinePK

Abstract

In recording the EEG signals are often contaminated by a large of signals called artifacts such that the brain activity (source) is difficult to be estimated. 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 effectively estimated the brain activity according to the given stimulus and remove the artifacts from all subjects.