The technique named detrended fluctuation analysis (DFA) has been used to reveal the presence of long-range temporal correlations (LRTC) and scaling behavior (SB) in electroencephalographic (EEG) recordings. The occurrence of these phenomena seems to be a salient characteristic of the healthy human brain and alterations in different pathologies has been described. Here we show how the filtering stages implemented in the systems for digital EEG influence the estimation of the DFA parameters used to characterize the brain signals. In consequence, we conclude that it is important to consider these filtering effects before interpreting the results obtained from digital EEG recordings.