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Feature extraction is an important issue of brain–computer interface (BCI). It determines whether the classification performance is high or low. In this paper, a new type of feature called “symmetrical feature” is proposed. This innovative feature extraction method is built upon the features “common spatial pattern (CSP)” algorithm. After an electroencephalographic signal is enhanced, class discrimination using the CSP algorithm can be extracted using optimal symmetrical axis chosen by a 10-fold cross-validation technique. Simulation results from nine data sets provided by brain–computer interface competition III and Iva showed that, on average, the proposed symmetrical feature can be combined with the CSP power band feature to boost the performance of the classification in a BCI system.

Many brain computer–interface (BCI) systems depend on imagined movement to control external devices. But how to extract the imagination feature and classify them to control systems is an important problem. To simplify the complexity of the classification, the power band and a small number of electrodes have been used, but there is still a loss in classification accuracy in the state-of-art approaches. The critical problem is the machine learning art that when the signal into source has property of non-stationary causing the estimation of the population parameter to change over time. In this paper, we analyzed the performance of feature extraction method using several spatial filter such as common average reference (CAR), Laplacian (LAP), common spatial pattern analysis (CSP) and no-spatial filter techniques and feature reduction method using principle component analysis (PCA) based 90% rule variance and leave-one-out correct classification accuracy selection method; where support vector machine is the classifier. The simulation with non-stationary data set from BCI competition III-Iva shows that CAR best performance CSP method in non-stationary data and PCA with leave-one-out CCA could maintain CCA performance and reduced the trading off between training and testing 13.96% compared to not using PCA and 0.46% compared PCA with 90% variance.