Advanced Brain-Controlled Actuation: Harnessing EEG Signals for Multi-Actuator Coordination In Robotics

The amalgamation of the (Brain-Computer Interface (BCI)) technology with robots has unveiled novel possibilities in assistive devices and sophisticated human-machine interactions. This study investigates the control of different robotic systems with mental instructions derived from EEG signals. The research shows a novel method of controlling 6 DOF robotic arms with four mental commands only, which can be applicable for higher DOF robotic arms. In addition, the EEG signals were also used to control the movement of 2 wheeled mobile robots. Emotive insight detects the signals from the brain. After analysing the signals, they are converted to mental commands. These commands are sent directly to Node-RED software directly in which this software works as a medium to transfer the brain signals directly to the Arduino controller and then the robot execution is processed according to the code uploaded. The system execution was performed in two types of environments, the Quiet and noisy environment. The execution of both systems was a success. Although the tests were only performed on a healthy individual ( the main author)due to time management of the PhD graduation, the system is totally applicable for unhealthy subjects for a reason of simplicity and negligible side effects. The accuracy achieved was good even with a noisy environment for complex movement of the robotic arm. In addition, the control of mobile robots runs smoothly with 70 % execution accuracy in noisy environments. This research emphasizes the prospective uses of BCI-controlled systems in prosthetics, assistive technologies, and remote robotic operations for people with physical limitations. The findings provide a realistic use of BCI technology, highlighting the viability of enhancing human-robot interactions via advanced signal processing and interface design. This study advances intuitive and accessible robotic systems by effectively commanding a complicated 6 DOF robotic arm and a mobile robot with just four mental instructions each. Future endeavours will concentrate on optimizing the system for practical applications, improving scalability, and investigating integration with more sophisticated robotic platforms and surroundings.