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Objective To find the related emotional characteristics with high recognition rate to predict depression by analyzing EEG signals.Methods Firstly, we collected the 14 channel EEG signals of 19 college students as the subject (female:6, male:13) under the standard IAPS affective pictures as the stimuli.The subjects were be divided into normal group and control group according to BDI.Secondly, EEG signals were collecting by Emotiv Epoc, and then EEGLAB toolbox was used to process the original data for removing the artifact signals.Finally, the time-frequency analysis were carried out by ERSP.Results For the negative stimuli, there were a significant difference (P<0.01) between normal and control groups, which occurred in the time range of 50-150 ms and 350-450 ms based on the Alpha and Beta bands.For the positive stimuli, there were a significant difference (P<0.01) between normal group and control group in the time range of 150-250 ms and350-450 ms based on the Alpha band, while, there were the significant difference (P<0.01) in the time range of 300-400 ms based on the Beta band.Conclusions There existed some significant characteristic of EEG with the time ranges between normal and control groups.These characteristic of EEG with the special time ranges may help researchers to recognize the depression tendency and provide certain reference basis for the depression research.