Empirical mode decomposition (EMD) has been successfully used in artifact suppression form the recorded electroencephalography (EEG) signals using a data-adaptive subband filtering approach. The higher computation burden of EMD processing is the main obstacle in online implementation of brain-computer interfacing (BCI). To resolve such limitation, multivariate wavelet transform with higher computation speed is introduced in this paper to decompose multichannel EEG signals into a finite set of subbands. The energy based subband filtering is implemented to separate the higher frequency noise components to clean the noisy event-related potential (ERP) signals. An auditory oddball BCI experiment is conducted to test cleaning performance followed by the BCI classification of single trial ERP using linear discriminant analysis (LDA). The experimental results illustrate that the classification performance is increased noticeably with the cleaned single-trial ERP data using proposed algorithm. It requires lower computational cost compared to EMD based cleaning approach.