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In general, the electrical activity of the brain is recorded using Electroencephalography (EEG), which is contaminated with some signal artifacts. By using automatic removal of artifacts from EEG signals, different Brain-Computer Interface (BCI) and clinical diagnostics applications are in practice. However, they are not efficient to remove the artifact from the EEG signal. Thus, we plan for the intelligent model for artifacts removal of EEG signal. The two main phases of the proposed model are training and testing. The deep learning model in the training phase is used as the filter to automatically remove noise from the contaminated EEG signal. The proposed model adopts improved One-Dimensional Convolution Neural Networks (1D-CNN) for artifacts removal from EEG signals. Here, a new hybrid algorithm named Spider Monkey-based Electric Fish Optimization (SM-EFO) is proposed by integrating the Spider Monkey …