In this work the algorithms are divided into three groups. First group consists of one method as Subspace Regularization Method (SRM). Second group consists of the combination both Wiener Filter (WF) & Subspace Method (SM) and Coherence Weighted Wiener Filter (CWWF) & SM in different order. The application of SM to filtered data with WF is proposed in literature, in this study we propose the reverse of this method using with the CWWF instead of WF. Third group consists of adaptive filters (Adaptive Kalman Filter (AKF), RLS and LMS). We propose two new methods including the combination of SM & AKF and SM & LMS Filter in 3(rd) group. All methods belong to 2(nd) and 3(rd) group are compared to each other and then best of each group are compared to SRM and Ensemble Averaging (EA) quantitatively. The highest SNR values are supplied with SRM among all methods mentioned in this work for small number of trials. However, the combination both SM & CWWF and the combination both SM & AKF provide higher output SNR values than EA, also. All the combination both SM and one method increase the advantages than each of them separately.