Blind Source Separation (BSS) approaches for multi-channel EEG processing are popular, and in particular Independant Component Analysis (ICA) algorithms have proven their ability for artifacts removal and source extraction for this very specific class of signals. However, the blind aspect of these techniques implies well-known drawbacks. As these methods are based on estimated statistics from the data and rely on an hypothesis of signal stationarity, the length of the window is crucial and has to be chosen carefully: large enough to get reliable estimation and short enough to respect the rather non-stationary nature of the EEG signals. In addition, another issue concerns the plausibility of the resulting separated sources. Indeed, some authors suggested that ICA algorithms give more physiologically plausible results depending on the chosen whitening/sphering step. In this paper, we address both issues by comparing three popular ICA algorithms (namely FastICA, Extended InfoMax and JADER) on EEG-like simulated data and assessing their performance by using an original correlation matrices distance measure and a separation performance index. The results are consistent and lead us to a precise idea of minimal sample size that guarantees statistically robust results regarding the number of channels.