Ocular Reduction in EEG Signals Based on Adaptive Filtering, Regression and Blind Source Separation

Abstract

Quantitative electroencephalographic (EEG) analysis is very useful for diagnosing dysfunctional neural states and for evaluating drug effects on the brain, among others. However, the bidirectional contamination between electrooculographic (EOG) and cerebral activities can mislead and induce wrong conclusions from EEG recordings. Different methods for ocular reduction have been developed but only few studies have shown an objective evaluation of their performance. For this purpose, the following approaches were evaluated with simulated data: regression analysis, adaptive filtering, and blind source separation (BSS). In the first two, filtered versions were also taken into account by filtering EOG references in order to reduce the cancellation of cerebral high frequency components in EEG data. Performance of these methods was quantitatively evaluated by level of similarity, agreement and errors in spectral variables both between sources and corrected EEG recordings. Topographic distributions showed that errors were located at anterior sites and especially in frontopolar and lateral–frontal regions. In addition, these errors were higher in theta and especially delta band. In general, filtered versions of time-domain regression and of adaptive filtering with RLS algorithm provided a very effective ocular reduction. However, BSS based on second order statistics showed the highest similarity indexes and the lowest errors in spectral variables.