A brain computer interface is a system that can translate the electrical activity of brain for using in communication and control.Such a translation is achieved invasively,e.g.,by measuring single neuron activities or non-invasively,e.g.,by recording electroencephalogram (EEG).We have to use some algorithm to analyze the EEG signal and to select the imagery feature characteristics for the sake of communicating with environment because of the low signal-to-noise ratio of these signals.In this paper we describe a new algorithm for the classification of motor imagery electroencephalogram (EEG).The algorithm is based on time-frequency analysis of EEG signals.The original EEG signals are converted to time-frequency signals by short time Fourier transforms (STFTs),then Fisher distance is used to select the features.