Artifact Removing from EEG Recordings using Independent Component Analysis with High-Order Statistics

Abstract: Many methods have been proposed to remove artifacts from EEG recordings especially those arising from eye movements and blinks. Often regression in time and frequency domain on parallel EEG and electrooculographic recordings is used, but this approach can become problematic in some cases. Use of Principal Component Analysis (PCA) has been proposed to remove eye artifacts from multichannel EEG. This method is not effective when the activations from cerebral activity and artifacts have comparable amplitudes. In this paper it is presented a generally applicable method for removing a wide variety of artifacts from EEG recordings based on Independent Component Analysis (ICA) with high-order statistics. The method is applied with good results in the analysis of a sample lowpass event -related potentials (ERP) data.