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Human brain activity can be measured with high temporal resolution by recording the electric potentials on the scalp surface using imaging methods such as the electroencephalogram (EEG). The analysis of EEG data is difficult due to the fact that multiple neurons may be simultaneously active and the potentials from these sources are superimposed on the limited sensors. It is desirable to unmix the data into signals representing the behavior of the original individual neurons. This is a problem of underdetermined blind source separation (UBSS). Since EEG signals are non-stationary, in this paper a two-stage UBSS approach is proposed for the separation of EEG signals by taking advantage of the high resolution of time-frequency distributions. Experimental results indicate the effectiveness of the introduced approach at separating EEG signals in the time-frequency domain compared with independent component analysis (ICA).