Brain activity is analyzed with the help of EEG signals. It has small amplitude, thus it is influenced by the various artefacts. It is highly needed that the artefacts should get eliminated from the EEG signals by efficacious processing. This paper explores the technicality of deep learning in order to remove the artefacts. For which Pre-processing and feature extraction is to be carried out initially for the EEG signals. Here the wavelet transform is applied to extract the wavelet features, which are scattered to the projected classifier which is called killer whale fractional calculus optimization (KWFCO). The technique is carried out with experimentation for removing artefacts like EMG, EOG, ECG and random noise on the EEG signal. The proposed technique's simulation results have been presented, and they have been found to perform well with improvement in MSE and SNR.