Using a multichannel Wiener filter to remove eye-blink artifacts from EEG data. (August 2018) - Borowicz, Adam
Highlights: Eye-blinks present serious problems for analysis of electroencephalographic data. Artifactual components can be estimated using multichannel Wiener filter. Extra electrooculographic electrodes are not necessary. Observation data can be enhanced in accordance with regression analysis model. Useful information is not removed from electroencephalographic data. New approach is better suited to real-time systems than the state-of-the-art method. Abstract: This paper presents a novel method for removing ocular artifacts from EEG recordings. The proposed approach is based on time-domain linear filtering. Instead of directly estimating the artifact-free signal, we propose to obtain the eye-blink signal first, using a multichannel Wiener filter (MWF) and a small subset of the frontal electrodes, so that extra EOG sensors are unnecessary. Then, the estimate of the eye-blink signal is subtracted from the noisy EEG signal in accordance with principles of regression analysis. We have performed numerical simulations so as to compare our approach to the independent component analysis (ICA) that is commonly used in EEG enhancement. Our experiments show that the MWF-based approach can perform better than the ICA in terms of eye-blink cancellation and signal distortions. Besides that, the proposed approach is conceptually simpler and better suited to real-time applications.