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. P300 wave, which is the most positive peak 260-410ms in an EEG signal after stimulus onset, is used as a control signal of the speller. The P300 wave has been separated using a blind source separation method in the existing P300 spellers. However, the conventional methods could not separate a source signal with Gaussian distribution from a set of mixed signals. To overcome this problem, we apply a kernel independent component analysis algorithm to P300 speller. The algorithm can successfully extract P300 component from a mixed signal even when it has source signals with nearly Gaussian distribution. In conclusion, the proposed P300 speller has 100% accuracy with less training signals and finds a target character more quickly than the conventional method.
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