Threshold-Based Electroencephalography Brain-Computer Interface for Robot Arm Control

Brain-Computer Interface (BCI) is a technique that uses real-time brain impulses to connect with and control external devices. BCI provides a new method for controlling external devices by translating brain signals into computer commands, facilitating the daily lives of people with disabilities and enhancing their ability to exhibit expected behavior. A Brain-Computer Interface (BCI) system based on Electroencephalography (EEG) was built to control the robotic arm. The EEG signals utilized included both eyes blinking, the right eye, the left eye, and the jaw contraction. EEG data were recorded from seven healthy subjects. The threshold approach is used to classify EEG signals, with the feature employed being the amplitude of the EEG signal. The highest threshold value for the blinking signal was 0.6 mV with an accuracy of 97.9%, while the best threshold value for jaw contraction was 0.4 mV with an accuracy of 93.34 percent. The healthy, inexperienced participants took part in system testing. The total results of testing each robot movement yielded an overall success rate of 84.52 percent. Therefore, it was determined that the system could facilitate the operation of the length robot even if the user lacked prior experience with EEG-based systems.