Restoring Engagement in Human-Robot Interaction: A Brain-Computer Interface for Adaptive Learning with Robots

This paper investigates the efficacy of a passive Brain-Computer Interface (BCI) in enabling a robot tutor to adaptively respond to a user's engagement level in real-time. The BCI system extracted EEG Engagement Index from the user's electroencephalography (EEG) signals as an indicator of engagement during Human-Robot Interaction (HRI). A within-subjects study was conducted in which the robot performed attention-recapturing behavior during a learning task under two conditions; either in an adaptive manner whenever a lapse in the user's engagement level was detected by the BCI system (Adaptive condition) or at random intervals regardless of the user's mental states (Random condition). In both conditions, users completed an information retention test following the interaction. The study found no significant difference in the postinteraction test results or mean EEG Engagement Index values between the Adaptive and Random conditions. However, analysis of 10-sec time windows following robot interventions showed that adaptively timed gestures were significantly more effective in restoring user engagement to optimal level compared to randomly timed gestures. This finding provides evidence for the potential of passive BCIs in improving user experience in pedagogical HRI settings.