Abstract: The motor imagery ECoG(electrocorticongraph) was investigated, specifically for classifying different imagined movements of the left little finger and tongue through ECoG, with BP(band power) of the ECoG signal extracted as the feature of BCI2005 competitive dataset I. Then Fisher, L0, and SVM-RFE were each used to select the best features. After a 10-fold cross validation of the training dataset, the features selected by SVM-RFE were the best of the three feature selection methods because it provided the lowest classification error rate and the least feature dimensionality. In addition, selected features of the training dataset were used to train a linear SVM model while selected features of the testing dataset were used to predict the labels by the model. Final classification accuracy was 94%.