A novel time-frequency analysis method based on Fisher criterion for optimization of movement-related EEG power features is proposed to provide a theoretical guide for selecting the most relevant EEG frequency components. With Morlet wavelet filter to extract the optimized movement-related EEG features

two classes of EEG patterns for 4 subjects are discriminated and the average maximum classification accuracy reaches to 87.95%. By the two evaluation indexes

i.e. maximum classification accuracy and mutual information(MI)

the effectiveness of feature optimization based on time-frequency analysis of Fisher-ratio for improving classification performance is verified. The experimental results indicate the further applications of the propose method to the movement related EEG feature component selection and optimization.