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In this paper, a novel method was proposed based on wavelet packet decomposition (WPD) for extracting the brainwave features from EEG signals, and support vector machines (SVMs) for mental task classification.Besides, an original voting mechanism was put forward for the final decision of classification.Moreover, some useful preprocessing methods were also applied.Segmentation along the time axis for fast response and increasing the correct classification rate, nonlinear and linear normalization for emphasizing the important information in small magnitude and optimizing data distribution.Furthermore, an especial grouping method was put forward to realize optimizing several parameters automatically.The simulation results have proved the effectiveness of the proposed method.