Intelligent Control of Robotic Arm Using Brain Computer Interface and Artificial Intelligence

Abstract

The combination of signal processing and Artificial Intelligence (AI) is revolutionizing the robotics and automation industry by the deployment of intelligent systems and reducing human intervention. Reading human brain signal through electroencephalography (EEG) has provided a new direction of research that automate machines through the human brain and computer interface or Brain–Computer Interface (BCI). The study is also inspired by the same concept of intelligently controlling a robotic arm using BCI and AI to help physically disabled individuals. The proposed system is non-invasive, unlike existing technologies that provide a reliable comparison of different AI-based classification algorithms. This paper also predicts a reliable bandwidth for the BCI process and provides exact placements of EEG electrodes to verify different arm moments. We have applied different classification algorithms, i.e., Random Forest, KNN, Gradient Boosting, Logistic Regression, SVM, and Decision Tree, to four different users. The accuracy of all prescribed classifiers has been calculated by considering the first user as a reference. The presented results validate the novel deployment, and the comparison shows that the accuracy for Random Forest remained optimal at around 76%, Gradient Boosting is around 74%, while the lowest is 64% for Decision Tree. It has been observed that people have different activation bandwidths while the dominant frequency varies from person-to-person that causes fluctuations in the EEG dataset.