Since the past years, the world is transformed into a digital society, where every individual is living with a unique digital identifier. The primary purpose of this identifier is to distinguish from others as well as to deal with digital machines which are surrounding the world. Recently, many researchers proved that the brain electrical activity or electroencephalogram (EEG) signals could provide robust and unique features that can be considered as a new biometric authentication technique. One of the most important things to extract the efficient and unique features from the input EEG signals is to find the optimal method to decompose the input EEG signals. Therefore, this paper proposed a novel method for EEG signal denoising based on multi-objective flower pollination algorithm with wavelet transform (MOFPA-WT) to extract such information from denoised signals. MOFPA-WT is evaluated using a standard EEG signal dataset, namely, Keirn EEG dataset, which has five mental tasks, includes baseline, multiplication two numbers, geometric figure rotation, letter composing, and visual counting. The performance of MOFPA-WT is evaluated using three criteria, namely, accuracy, true acceptance rate, and false acceptance rate. It is worth mentioning that the proposed method achieves the highest accuracy result which can be obtained using mental tasks based on geometric figure rotation compared with mental tasks. © 2018 IEEE.