An improved SVM-based real-time P300 speller for brain-computer interface

We present a novel one-class classification method called conformal-kernel support vector data description (CK-SVDD) for P300 speller brain-computer interface (BCI). The CK-SVDD has lower computational complexity than the widely-used SVM, and better classification accuracy than both SVM and SVDD, thus being able to improve the usability of the P300 speller when it is used in an online mode. We also developed a real-time EEG acquisition and preprocessing module, as well as the software written in C#, which executes the functions of data processing and classification. The results, carried out on three subjects, show that the proposed CK-SVDD consistently performs better than the SVM and the original SVDD in different numbers of rounds. The results also indicate that it can achieve a high character classification accuracy of over 95% for all subjects, even when the number of rounds is less than 4 in some cases, thus being able to provide a fast online testing speed without losing classification accuracy.