EWT-Domain Optimized Modified LoG Filtering for Motion Artifact Suppression from EEG Signals

Abstract

Ambulatory electroencephalography (EEG) is a comparatively recent technology and a gold standard for the diagnosis of brain activity via prolonged EEG measurements. In general, motion artifact is the major concern during the ambulatory EEG signal acquisition. Removal of motion artifacts and other noise components from these EEG measurements has been a challenging task for researchers. In this paper, we propose a robust approach by employing an optimized Laplacian of Gaussian (LoG) filtering technique and empirical wavelet transform (EWT) to suppress the motion artifacts from ambulatory EEG measurements. The artifacts are suppressed via multi-resolution filtering of desired frequency components using optimized LoG filtering. Furthermore, the denoising performance of the proposed method is enhanced by optimizing the filter order. The efficacy of the proposed approach is evaluated using signal-to-noise ratio and correlation coefficient based metrics. The proposed method seems to have superior performance as compared to state-of-the-art techniques. © 2023 IEEE.