Symmetrical feature for interpreting motor imagery EEG signals in the brain–computer interface

Abstract 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.