A reduced-complexity P300 speller based on an ensemble of SVMs

A brain computer interface (BCI) system is to control a computer using bio-signals measured in brain. A P300 speller is one of electroencephalogram (EEG)-based BCI systems. The speller is to display target characters which are what a subject wants to enter by detecting P300 wave. To detect the wave, a lot of EEG signals were averaged over the whole signals to increase the signal-to-noise ratio and the support vector machine (SVM) was applied to a P300 speller to separate EEG signals with P300 wave and without P300 wave in previous works. In current classifier topics, there are some methods to average some classifiers for performance improvement. An ensemble of SVMs is one of them but it has enormous computational complexity. To overcome this computational burden, we propose a P300 speller with preprocessing of channel selection and non-target data reduction. In conclusion, the calculation speed becomes higher than conventional method but, as a feature dimension decreases in channel selection part of the proposed method, the accuracy of the proposed method is lowered in both subjects.