garakani, golnoosh, Ghane, Hamed, Menhaj, Mohammad Bagher. (1398). Control of a 2-DoF robotic arm using a P300-based brain-computer interface. نشریه مهندسی عمران امیرکبیر, 51(2), 153-162. doi: 10.22060/miscj.2019.15569.5136
golnoosh garakani; Hamed Ghane; Mohammad Bagher Menhaj. "Control of a 2-DoF robotic arm using a P300-based brain-computer interface". نشریه مهندسی عمران امیرکبیر, 51, 2, 1398, 153-162. doi: 10.22060/miscj.2019.15569.5136
garakani, golnoosh, Ghane, Hamed, Menhaj, Mohammad Bagher. (1398). 'Control of a 2-DoF robotic arm using a P300-based brain-computer interface', نشریه مهندسی عمران امیرکبیر, 51(2), pp. 153-162. doi: 10.22060/miscj.2019.15569.5136
garakani, golnoosh, Ghane, Hamed, Menhaj, Mohammad Bagher. Control of a 2-DoF robotic arm using a P300-based brain-computer interface. نشریه مهندسی عمران امیرکبیر, 1398; 51(2): 153-162. doi: 10.22060/miscj.2019.15569.5136

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

AUT Journal of Modeling and Simulation
مقاله 8، دوره 51، شماره 2، اسفند 2019، صفحه 153-162    اصل مقاله (1.29 M)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22060/miscj.2019.15569.5136
نویسندگان
golnoosh garakani1؛ Hamed Ghane* 2؛ Mohammad Bagher Menhaj3
1university of tehran
2Isalmic Azad University Bandar-e-Anzali Branch
3Electrical Engineering, Amirkabir University of Technology
چکیده
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 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 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.
کلیدواژه‌ها
Brain-computer interface (BCI)؛ EEG؛ P300 Potential؛ Classification؛ 2-DoF robotic arm
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