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

Control of a 2-DoF robotic arm using a P300-based brain-computer interface