Analysis of electroencephalographic (EEG) data requires cautious consideration of interfering artefacts such as ocular, muscular or cardiac noise. Independent component analysis (ICA) has proven to be a powerful tool for the detection and separation out of these contaminating components from brain activity. Yet thus far thorough investigation is lacking into how this pre-processing step might affect or even distort the information on brain connectivity inherent in the raw signals. The aim of this work is to address this question by systematically investigating and comparing three different strategies: first, analysis of all network nodes without eliminating contamination; second, removing the node which is contaminated by artefacts; third, using the ICA artefact removal method as an initial step prior to the analysis. Multivariate, time-variant autoregressive models are used to approximate the recorded data; the assessment of information flow within the modelled networks is carried out by means partial directed coherence, offering a frequency-selective estimation of connectivity.