BSS is one of the well-known methods of signal processing. This method is based on recovering of original sources from observed mixtures without any further information about mixing system and original sources. In many applications, mixtures are combination of non-Gaussian and time-correlated components. MCOMBI algorithm is known as a method for separation of these kinds of sources. The performance and accuracy of this algorithm are noticeable but the high computational cost is one of the most significant limitations of MCOMBI algorithm, especially for high-dimensional data sets like high-density electroencephalographic (EEG) or magnetoencephalographic (MEG) recordings. In this chapter, we propose a new algorithm which uses combination of WASOBI and EFICA algorithms. In addition we use clustering method to decrease computational cost. In contrast with MCOMBI algorithm, the proposed algorithm decreases run time of separation and it has high accuracy close to MCOMBI algorithm. Thus, this algorithm is suitable for real high-dimensional data sets. In this chapter we use our algorithm for separation of artifacts in real MEG data.