  In this study, a novel control algorithm for a P-300 based brain-computer
interface is fully developed to control a 2-DoF robotic arm. Eight subjects
including 5 men and 3 women, perform a 2-dimensional target tracking task in a
simulated environment. Their EEG signals from visual cortex are recorded and
P-300 components are extracted and evaluated to perform a real-time BCI based
controller. The volunteer's intention is recognized and will be decoded as an
appropriate command to control the cursor. The final goal of the system is to
control a simulated robotic arm in a 2-dimensional space for writing some
English letters. The results show that the system allows the robot end-effector
to move between arbitrary positions in a point-to-point session with the
desired accuracy. This model is tested on and compared with Dataset II of the
BCI Competition. The best result is obtained with a multi-class SVM solution as
the classifier, with a recognition rate of 97 percent, without pre-channel
selection.
