Denoising is an important preprocessing stage in some ElectroEncephaloGraphy (EEG) applications. For this purpose, Blind Source Separation (BSS) methods, such as Independent Component Analysis (ICA) and Decorrelated and Colored Component Analysis (DCCA), are commonly used. Although ICA and DCCA-based methods are powerful tools to extract sources of interest, the procedure of eliminating the effect of sources of non-interest is usually manual. It should be noted that some methods for automatic selection of artifact sources after BSS methods exist, although they imply a training supervised step. On the other hand, in cases where there are some a priori information about the subspace of interest, semi-blind source separation methods can be used to denoise EEG signals. Among them the Generalized EigenValue Decomposition (GEVD) and Denoising Source Separation (DSS) are two well-known semi-blind frameworks that can be used with a priori information on the subspace of interest. In this paper, we compare the ICA and DCCA-based methods, namely CoM₂ and SOBI, respectively, with GEVD and DSS in the application of extracting the epileptic activity from noisy interictal EEG data. To extract a priori information required by GEVD and DSS, we propose a series of preprocessing stages including spike peak detection, extraction of exact time support of spikes and clustering of spikes involved in each source of interest. The comparison of these four methods in terms of performance and numerical complexity shows that CoM₂ give better performance for very low SNR values but require visual inspection to select the sources of interest. For higher SNR values, GEVD and DSS based approaches give similar results but with lower numerical complexity and without requiring a visual selection of the sources of interest.