A dual-class voting mechanism for brain computer interface based on wavelet packet and support vector machine

An original dual-class voting mechanism was put forward as the final decision method for brain computer interface, which is based on wavelet packet decomposition (WPD) to extract the brainwave features from EEG signals, and support vector machines (SVMs) to classify five mental tasks. Moreover, several preprocessing methods were applied efficiently. Segmentation along the time axis for increasing the correct classification rate, and nonlinear as well as linear normalization for emphasizing the important information in small magnitude and optimizing data distribution. Further, an especial grouping method was proposed to realize optimizing parameters automatically. Approximately, 95% of correct classification rate is obtained based on the proposed method, which is higher than the conventional.