Yu-Ri Lee, Ju-Yeong Lee, Hyoung-Nam Kim
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Brain-computer interfaces; EEG; P300; ICA.
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.