Intelligentocular artifacts removal in a noninvasive singlechannel EEG recording

Abstract

Muscle noises, line noises and eye movements are the main interferences that make difficulties when interpreting and analyzing electroencephalographic signals. Many methods have been proposed for artifacts removing from EEG measurements, and especially those arising from an ocular source. Principal Component Analysis (PCA) and Independent Component Analysis (ICA) have been proposed to remove ocular artifacts from multichannel EEG. In contrast to this, we present a new algorithm for ocular artifacts removal from a single electroencephalographic channel recording. This method is based on a set of information on brain wave frequencies. Our results on EEG data, collected from healthy subjects, show that our algorithm can effectively detect and remove ocular artifacts in EEG recordings.