The Removal of Ocular Artifacts from EEG Signals Using Adaptive Filters Based on Ocular Source Components

Abstract

Ocular artifacts are the most important form of interference in electroencephalogram (EEG) signals. An adaptive filter based on reference signals from an electrooculogram (EOG) can reduce ocular interference, but collecting EOG signals during a long-term EEG recording is inconvenient and uncomfortable for the patient. In contrast, blind source separation (BSS) is a method of decomposing multiple EEG channels into an equal number of source components (SCs) by independent component analysis. The ocular artifacts significantly contribute to some SCs but not others, so uncontaminated EEG signals can be obtained by discarding some or all of the affected SCs and re-mixing the remaining components. BSS can be performed without EOG data. This study presents a novel ocular-artifact removal method based on adaptive filtering using reference signals from the ocular SCs, which avoids the need for parallel EOG recordings. Based on the simulated EEG data derived from eight subjects, the new method achieved lower spectral errors and higher correlations between original uncorrupted samples and corrected samples than the adaptive filter using EOG signals and the standard BSS method, which demonstrated a better ocular-artifact reduction by the proposed method.