Abstract: To explore a practical way of Brain-Computer Interface（BCI） based on imaging movement，and to extract features of Electroencephalography（EEG） for reflecting different thoughts by searching suitable methods of signal extraction and recognition algorithm，and to enhance recognition rate of communication for BCI system，to establish a substantial theory and experimental foundation for the application of BCI，different mental tasks based on imaging left-right hand movement from 6 subjects were studied at three different time sections （hinting keying after arrow appearing at 2 s，1 s and 0 s）.Then authors used Wavelet Transform（WT） and Support Vector Machine（SVM） methods to process and analyze the off-line experimental data.Average delay time Δt₂，Δt₁ and Δt₀ for all subjects at three different time sections were analyzed，it was discovered that there was significant difference between Δt₀ and Δt₂ or Δt₁ （p<0.05），but there was no significant difference between Δt₂ and Δt₁ （p>0.05）.The average results of classification rate were 68.00％，80.00％ and 56.67％ （p<0.05），respectively.There are obviously different features for imaging left-right hand movement about 0.5~1 s before practical action，these features have significant difference.Authors obtained higher classification rate of communication under hinting keying after arrow appearing about 1 s.These results show it is helpful to increase the correct rate by reasonable experimental design.The features extraction method proposed in this study has been proven feasible to be used as external control signals for BCI system.This study provides new ideas and methods for features extraction and classification of different mental tasks for BCI.