The paper presents a Brain-Computer Interface (BCI) controller for a semiautonomous three-wheeled omnidirectional robot capable of processing real-time commands. The kinematical model of the omni-directional robot and the software architecture of the overall hybrid system with motion control algorithm are presented. The system design, acquisition of the electroencephalography (EEG) signal, recognition processing technology and implementation are the main focus. Signals are recorded and processed by a program called OpenVibe. Preprocessed signals are cleaned by EEGLAB and used to train OpenVibe classifiers to accurately identify the expected signals produced by the users. Once identified, the controller converts the signal into input commands {forward, left, right, rotate, stop}, which are written in the Python syntax and delivered to the robot system. The robot has three degrees of freedom (DoF) allowing it to traverse its environment in any direction and orientation. The sensor system provides feedback allowing for the semi-autonomous control to avoid obstacles. Overall, this paper demonstrates the architecture of the hybrid control system for omni-directional robot using BCI. The developed system integrates the EEG signal to control the motion of the robot and the experimental results show the system performance and effectiveness of possessing the user’s EEG signals.

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