Investigation of EEG Noise and Artifact Removal by Patch-Based and Kernel Adaptive Filtering Techniques

Abstract

Electroencephalography (EEG) signals are usually corrupted with several unwanted noise and artifact sources, which lead to poor signal quality and wrong clinical diagnosis. This paper investigates, for the first time, a 1-D implementation of the patch-based NonLocal Means (NLM) algorithm, which was used for 2-D image processing, in removing (1) the Additive White Gaussian Noise (AWGN) and (2) artifacts from EEG signals. A critical comparison between the NLM approach and one of the most effective adaptive filtering techniques, Kernel Recursive Least Squares (KRLS), is made for both removing the AWGN noise and artifact sources. This comparison is demonstrated by investigating several EEG datasets using different evaluation metrics such as the Signal Noise Ratio (SNR), Mean Square Error (MSE), cross correlation, and computational time. The results show that the NLM-based approach more effectively reduces the AWGN/colored noise than the KRLS adaptive filtering. The results also reveal that the NLM approach fails to remove the Electrocardiogram (ECG) artifact in an artifacts contaminated EEG signal, while the KRLS adaptive filter significantly removes most of this ECG component. This reveals that the patch-based techniques are efficient and promising in removing the AWGN/colored noise sources but they are less successful in suppressing interference artifacts.