Abstract: This article aims to investigate mechanisms of the abnormality in emotional processing for depression patients by comparing their sample entropy of the electroencephalogram (EEG) with normal controls. We recruited 16 patients with depression and 14 healthy controls to participate in a spatial search task for facial s. We collected their scalp EEG signals when they were performing the task. Then the Hilbert-Huang transform was used to obtain the activity of EEG. We investigate the EEG differences between the two groups by comparing the sample entropy of the activity of EEG in the two groups. Finally, β-band sample entropy is used as a feature to classify with different classifiers and different extraction methods. our results demonstrated abnormal beta activity in EEG with depression during their emotional processing, especially for the positive emotion. Moreover, this study supports that the sample entropy is a potential tool to reflect the specificity of emotional EEG. It can be used to classify depression patients and provide an assistant feasible solution for doctors to diagnose patients with depression.