Influence of ocular filtering in EEG data on the assessment of drug-induced effects on the brain.

Ocular artifacts in EEG signals affect the interpretation of clinical study results. The aim of this study was to assess the influence of automatic ocular filtering procedures in the conclusions drawn from a pharmaco-EEG trial. Regression analysis, gold standard, and blind source separation (BSS), Second Order Blind Identification algorithm, ocular filtering procedures were compared using time, frequency, topographic and tomographic brain mapping approaches and pharmacokinetic-pharmacodynamic (PK-PD) relationships. Data consisted of EEGs obtained from 20 volunteers who received single oral doses of haloperidol 3 mg, risperidone 1 mg, olanzapine 5 mg and placebo in a randomized cross-over double-blind design. Although the BSS-based technique preserved brain activity more than regression analysis in anterior leads, in general, topographic significance probability maps globally showed similar results with both methods for most spectral variables. However, different results were obtained when using whole multi-channel information for studying drug effects in the brain: (i) higher correlations between PK and PD time courses showing that BSS allowed estimation of spectral variables more accurately related to drug effects and (ii) larger and more symmetric drug related tomographic LORETA maps showing that BSS led to results that were more neurophysiopharmacologically sound. Definitely, the BSS-based procedure is an effective and efficient preprocessing method to remove ocular artifacts from EEG data. The selection of the ocular filtering procedure could determine different results whose impact depends on the evaluating tool applied to analyze the pharmaco-EEG data. (c) 2008 Wiley-Liss, Inc.

RCT Entities:   Population Interventions Outcomes