In order to realize brain-computer interface (BCI), optimal features of single trail motor imagery electroencephalogram (EEG) were extracted and classified. Mu rhythm of EEG was obtained by preprocessing, and the features were optimized by spatial filtering, which are estimated from a set of data by method of common spatial pattern. Classification decision can be made by Fisher criterion, and classification performance can be evaluated by cross validation and receiver operating characteristic (ROC) curve. Optimal feature dimension determination projected by spatial filter was discussed deeply in cross-validation way. The experimental results show that the high discriminate accuracy can be guaranteed, meanwhile the program running speed is improved. Motor imagery intention classification based on optimized EEG feature provides difference of states and simplifies the recognition processing, which offers a new method for the research of intention recognition.