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Artifacts Removal in EEG Signal Using a NARX Model Based CS Learning Algorithm

An Electroencephalogram (EEG) signal is essential clinical tool for monitoring the neurological disorders. The electrical activity of the EEG signal is obtained by placing several electrodes on the brain scalp. However, the recorded signals are easily affected by various artifacts which reduce its clinical convenience. In order to remove the artifacts signal such as EOG, EMG and ECG, we have proposed, a new nonlinear autoregressive with exogenous input (NARX) filter in this paper. Then, the efficient learning algorithm of cuckoo search (CS) algorithm is proposed for the elimination of various artifacts from the reordered EEG signal. Here, the performance of the proposed model is analysed using signal to noise ratio (SNR) and root mean square error (RMSE) value. Finally, results shows the effectiveness of the proposed model by extracting the artifcats signal from the recorded signals based on the maximum signal to noise ratio and minimum root mean square error value. From the results, we can conclude that the proposed model obtained the maximum SNR rate as 47.54db compared to various existing artifacts removal models such as independent component analysis (ICA), Fast independent component analysis (FICA), neural network model (NN).