EEG and Eye-Tracking Integration for Ocular Artefact Correction

Abstract

Electroencephalograms (EEG) are a widely used brain signal recording technique. The information conveyed in these recordings can be an extremely useful tool in the diagnosis of some diseases and disturbances, as well as in the development of non-invasive Brain-Machine Interfaces (BMI). However, the non-invasive electrical recording setup comes with two major downsides, a. poor signal-to-noise ratio and b. the vulnerability to any external and internal noise sources. One of the main sources of artefacts are eye movements due to the electric dipole between the cornea and the retina. We have previously proposed that monitoring eye-movements provide a complementary signal for BMIs. He we propose a novel technique to remove eye-related artefacts from the EEG recordings. We couple Eye Tracking with EEG allowing us to independently measure when ocular artefact events occur and thus clean them up in a targeted manner instead of using a "blind" artefact clean up correction technique. Three standard methods of artefact correction were applied in an event-driven, supervised manner: 1. Independent Components Analysis (ICA), 2. Wiener Filter and 3. Wavelet Decomposition and compared to "blind" unsupervised ICA clean up. These are standard artefact correction approaches implemented in many toolboxes and experimental EEG systems and could easily be applied by their users in an event-driven manner. Already the qualitative inspection of the clean up traces show that the simple targeted artefact event-driven clean up outperforms the traditional "blind" clean up approaches. We conclude that this justifies the small extra effort of performing simultaneous eye tracking with any EEG recording to enable simple, but targeted, automatic artefact removal that preserves more of the original signal.