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

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.

Tracking eye fixations with electroocular and electroencephalographic recordings.

We describe a method, based on recordings of the electroencephalogram (EEG) and eye movement potentials (electrooculogram), to track where on a screen (x,y coordinates) an individual is fixating. The method makes use of an empirically derived beam-forming filter (derived from a sequence of calibrated eye movements) to isolate eye motion from other electrophysiological and ambient electrical signals. Electrophysiological researchers may find this method a simple and inexpensive means of tracking eye movements and a useful complement to scalp recordings in studies of cognitive phenomena. The resolution is comparable to that of many commercial systems; the method can be implemented with as few as four electrodes around the eyes to complement the EEG electrodes already in use. This method may also find some specialized applications such as studying eye movements during sleep and in human-machine interfaces that make use of gaze information.