In this study, an EEG signal classification framework was proposed. The framework contained three feature extraction methods refer to optimization strategy. Firstly, we selected optimal electrodes based on the single electrode classification performance and combined all the optimal electrodes? data as the feature. Then, we discussed the contribution of each time span of EEG signals for each electrode and joined all the optimal time spans? data together to be used for classifying. In addition, we further selected useful information from original data based on genetic algorithm. Finally, the performances were evaluated by Bayes and SVM classifiers on BCI 2003 Competition data set Ia. And the accuracy of genetic algorithm has reached 91.81%. The experimental results show that our methods offer the better performance for reliable classification of the EEG signal.
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