Automatic removal of eye movement and blink artifacts from EEG data using blind component separation

TLDR

Eye movement and blink artifacts are orders of magnitude larger than brain potentials and cause substantial data loss when rejected, limiting experimental designs, while blind source separation (BSS) with independent component analysis (ICA) is a signal‑processing approach used to address such contamination. The study introduces a BSS‑based method for automatically removing electroocular artifacts from EEG data. The method employs blind source separation to isolate ocular components and can be extended to other EEG contaminants such as cardiac signals, environmental noise, electrode drift, and adapted for magnetoencephalographic data. Compared to prior ICA‑based approaches, the algorithm is fully automated and accurately isolates correlated electroocular components.

Abstract

Abstract Signals from eye movements and blinks can be orders of magnitude larger than brain‐generated electrical potentials and are one of the main sources of artifacts in electroencephalographic (EEG) data. Rejecting contaminated trials causes substantial data loss, and restricting eye movements/blinks limits the experimental designs possible and may impact the cognitive processes under investigation. This article presents a method based on blind source separation (BSS) for automatic removal of electroocular artifacts from EEG data. BBS is a signal‐processing methodology that includes independent component analysis (ICA). In contrast to previously explored ICA‐based methods for artifact removal, this method is automated. Moreover, the BSS algorithm described herein can isolate correlated electroocular components with a high degree of accuracy. Although the focus is on eliminating ocular artifacts in EEG data, the approach can be extended to other sources of EEG contamination such as cardiac signals, environmental noise, and electrode drift, and adapted for use with magnetoencephalographic (MEG) data, a magnetic correlate of EEG.