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

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