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 fully developed to control a 2-DoF robotic arm. Eight subjects including 5 men and 3 women perform a 2-dimensional target tracking in a simulated environment. Their EEG (Electroencephalography) signals from visual cortex are recorded and P300 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 processed BCI output is used to control a simulated robotic arm in a 2-dimensional space. The control algorithm is inverse dynamics control which is based on the exact linearization of all nonlinear dynamics of the system. 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. Furthermore, it should be noted that the proposed approach is suitable for BCI control applications. 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. In this project, the EEG device used to record data is Starstim.