Denoising simulated EEG signals: A comparative study of EMD, wavelet transform and Kalman filter

Electrooculographic (EOG) artefact is one of the most common contaminations of Electroencephalographic (EEG) recordings. The corruption of EEG characteristics from Blinking Artefacts (BAs) affects the results of EEG signal processing methods and also impairs the visual analysis of EEGs. In this paper, our scope was a comparative analysis of the performance of three standard denoising methods like continuous Empirical Mode Decomposition (EMD), Discrete Wavelet Transform (DWT) and Kalman Filter (KF). In order to evaluate the performance of EMD, DWT and KF of noise reduction and to express the quality of the denoised EEG, we calculate several indexes such as the Signal-to-Noise Ratio (SNR). All the results obtained from noise simulated EEG data show that WT achieved the greatest SNR difference and also the mode mixing issue of EMD affected this method's performance.

1. Introduction

Electroencephalogram (EEG) is a noninvasive measurement of the brain's electrical activity obtained using several electrodes placed on the scalp. Over the last decades and under an increasing medical demand, EEG became an important diagnostic tool for monitoring and managing dysfunctions and various neurological disorders of the human brain.