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This paper presents the classification of three mental tasks, using the electroencephalographic signal and simulating a real-time process, that is, the pseudo-online technique. Linear Discriminant Analysis is used to recognize the mental tasks, and the feature extraction uses the Power Spectral Density. The choice of EEG channel and frequency uses the Kullback-Leibler symmetric divergence and a reclassification model is proposed to stabilize the classifier. Finally, it is expected that the proposed method can be implemented in a Brain-Computer Interface associated with a robotic wheelchair.