Neuro-Robot Interaction in Robot-Assisted Surgery Using EEG and Self-Supervised Graph Transformer

Abstract

Robot-Assisted Surgery (RAS) represents a major frontier in the robotics community, blending precision automation with human skill in high-stakes clinical environments. Evaluating surgeon performance in RAS is critical for training and certification, yet current methods rely heavily on video analysis or subjective manual scoring. This study presents a neuro-robotic interaction framework that uses Electroencephalography (EEG)-derived brain connectivity features to classify surgeons’ skill levels during RAS tasks. The high dimensionality of EEG data imposes substantial computational cost. Therefore, we first apply Harris Hawks Optimization (HHO) to select an optimal EEG-channel subset, reducing computational cost. Then, functional connectivity feature metrics are extracted from the reduced EEG channel set and used to construct brain graphs, which serve as input to a Self-Supervised Graph Transformer (SSGT). The SSGT model is pre-trained via masked edge reconstruction to capture structural dependencies and finetuned for downstream skill-level classification. The proposed SSGT model achieves a classification accuracy of 96.60%, significantly outperforming both traditional machine learning and deep learning baselines. The label-efficient, structurally aware design of SSGT enables scalable and real-time assessment of surgical proficiency. This framework provides a foundation for intelligent robotic tutoring systems and generalizes to broader cognitive monitoring tasks in high-stakes human-robot interaction domains using EEG.