EEG-Based Brain-Computer Interface for Robotic Assistance with User Intention Prediction

Abstract Brain–computer interfaces (BCIs) translate neural signals into control commands to restore or augment human capabilities, enabling robotic assistance for essential daily activities. Compared with intracortical BCIs, non-invasive BCIs require less medical and surgical intervention, but have yet to demonstrate reliable performance in complex everyday tasks with high success and low mental effort. We present Electroencephalography (EEG)-based Neural Signal Operated Intelligent Robots (NOIR-EEG), a general-purpose, intelligent, non-invasive BCI framework that allows users to command robots via EEG signals. NOIR-EEG combines advances in neural signal decoding with recent progress in AI and robotics, including large pre-trained models and intention learning. In tests, sixteen participants successfully completed fifteen challenging household tasks. Intention learning algorithms adapt to individual users and predict their goals, substantially reducing human effort.