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The role of Working Memory (WM) as a notable characteristic in mental abilities and diagnosis of brain diseases have been survived widely in recent years. WM manipulates information for a short time and dose multi-task. In this paper, the significant factors of WM have been extracted by wavelet coefficients from EEG signals. 12 pictures were shown to subjects randomly and asked them to review these pictures in order to focus on WM task. The analysis is concentrated on the differences between EEG signals of the WM task and those of the rest mood, by means of the Repeated Measures ANOVA. The results show significant reduction in standard deviation of some wavelet coefficients in WM task in contrast with rest mood. Also importance of alpha (8-12Hz) and theta (4-8Hz) oscillations which matches with pervious related studies outcome is shown.