control of a 2-dof robotic arm using a p300-based brain-computer interface

In this study, a novel control algorithm, based on a p300-based brain-computer interface (bci) is deployed to control a 2-dof robotic arm. eight subjects, including five men and three women, perform a 2-dimensional target tracking in a simulated environment. their eeg (electroencephalography) signals from the visual cortex are recorded and p300 components are extracted and evaluated to deliver a realtime bci-based controller. the volunteer’s intention is recognized and will be decoded as an appropriate command to control the cursor. the final processed bci output is used to control a simulated robotic arm in a 2-dimensional space. the results show that the system allows the robot’s end-effector to move between arbitrary positions in a point-to-point session with the desired accuracy. this model is tested and compared on the dataset ii of the bci competition. the best result is obtained with a multi-classifier solution with a recognition rate of 97 percent, without channel selection before the classification.