Developing an Electroencephalogram Based Robotic Motion Control System Using Brainwaves Enabled Signal Processing Technique

This work presents the development of an Electroencephalogram (EEG)-based robotic motion control system utilizing brainwave-enabled signal processing techniques. The system is designed to translate mental commands into real-time robotic actions, offering a non-invasive brain-computer interface (BCI) for users. The system involves EEG signal acquisition, preprocessing, feature extraction, and classification using machine learning algorithms, followed by robotic control via a real-time interface. Data was collected from 10 participants performing four mental tasks: move forward, turn left, turn right, and stop. The classification accuracy across these tasks ranged from 84% to 94%, with an average accuracy of 88%. Response times for translating mental commands into robotic actions varied between 205 milliseconds and 280 milliseconds, ensuring near real-time control. Testing under different environmental conditions (normal, noisy, fatigued) showed the system's robustness, although there was a slight drop in accuracy under noisy (80-85%) and fatigued (81-88%) conditions. User satisfaction scores indicated a positive overall experience with high ratings for ease of use and perceived accuracy. The results demonstrate the system's potential for applications in assistive technologies and rehabilitation robotics.