A Comparison of Wiener and Kalman Filters for the Artifact Suppression from EEG Signal

Electroencephalogram (EEG) is a noninvasive method to record electrical activity of brain and it has been used extensively in research of brain function due to its high time resolution. However raw EEG is a mixture of signals, which contains noises such as Ocular Artifact (OA) that is irrelevant to the cognitive function of brain. To remove OAs from EEG, many methods have been proposed, such as Independent Components Analysis (ICA), Empirical Mode Decomposition (EMD), Discrete Wavelet Transform (DWT), Adaptive filtering and Adaptive Noise Cancellation (ANC). In this paper, a comprehensive overview of techniques that can be used for the removal of artifacts from an EEG. For this purpose, the Wiener and Kalman filters are used to compare to remove OAs in EEG. Firstly, the artifact removing method using two filters are applied on synthetic data. Two factors are used to compare the result of filter on EEG signal, that are Signal to Artifact Ratio (SAR) and Mean Squared Error (MSE). The SAR value is 4.34 dB for Kalman filter while for Wiener filter it is 5.30 dB. The Mean Squared Error (MSE) of Weiner filter is 7.195x10-05, significantly lower compared to results using Kalman method is 0.067. Then these approaches is applied to the contaminated EEG data. The experimental result shows that comparatively the Wiener filter is more effective in removing the artifact without losing the original information.