Removing noise and artifacts from EEG using adaptive noise cancelator and blind source separation

Abstract

Electroencephalography (EEG) is one of the most effective diagnostic procedure for epilepsy. However, the presence artifacts like electro-oculogram (EOG), electrocardiogram (ECG), electromyogram (EMG) and powerline noise 50hz in the EEG signal is a major problem in the study of brain potentials. A variety of algorithms have been proposed to reject these artifacts and noise including methods based on regression and blind source separation (BSS) techniques. In this study, the performances of two widely used artifact rejection techniques are presented.One based on Least Mean Square Adaptive noise cancellation (ANC) for removing ECG artifact and powerline noise. And the another is BSS technique which uses the Second Order Blind Identification (SOBI) to reject EMG and EOG artifacts. Each algorithm was applied in real EEG data and then their performance quantified in the time domain. © Springer-Verlag 2010.