In this paper we evaluate the performance of 5 BSS algorithms (AMUSE, SOBI, SOBI-RO, SONS, JADE-TD)on simulated EEG signals. A first reesult evaluates the influence of the noise and signal characteristics (frequency, length, SNR) on the algorithms performance. A second objetive is to introduce a new performance criterion, IEV which can be use to compare two matrices and is potentially usefull on real signals. We validate this new index by comparing it with classic performance indices used in source separation.