During the acquisition time, electroencephalography (EEG) signals suffer from motion and eye blink artifacts. There is various unsupervised learning based artifact removal methods were designed in literature. But most of existing methods are unable to remove the high peaks of eye blink artifacts. The goal is to create a filter that suppresses all motion artifacts at the same time