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The non-linearity and non-stationary characteristics of EEG signal make it difficult to extract features and classify them into different categorizes. This paper proposes a classification method of EEG signals based on empirical mode decomposition(EMD) and support vector machine(SVM), which makes full use of the adaptive ability of EMD algorithm in dealing with nonlinear and non-stationary signals, as well as the high identification performance and strong generalization ability of SVM in small sample conditions. Firstly, C3 and C4 lead signals are decomposed into a series of Intrinsic Mode Function (IMF) by EMD algorithm, and then features are extracted from IMF"s information and energy dimensions to transform EEG signals into more discriminable feature domain, and finally SVM is utilized to classification EEG signals into different categorizes. Using Graz data from international BCI competition 2003, the proposed method can achieve a correct recognition rate of 94.6%, which providing a new idea for the study of online brain-computer interface system.