A Noval approach for EEG signal artefact removal using Deep convolutional Algorithm

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 shouldget eliminated from the EEG signals by efficacious processing. This paper explores thetechnicality of deep learning in order to remove the artefacts. For which Pre-processingand feature extraction is to be carried out initially for the EEG signals. Here the wavelettransform is applied to extract the wavelet features, which are scattered to the projectedclassifier which is called killer whale fractional calculus optimization (KWFCO). Thetechnique is carried out with experimentation for removing artefacts like EMG, EOG,ECG and random noise on the EEG signal. The proposed technique's simulation resultshave been presented, and they have been found to perform well with improvement in MSEand SNR.