Trust Assessment with EEG Signals in Social Human-Robot Interaction

The role of trust in human-robot interaction (HRI) is becoming increasingly important for effective collaboration. Insufficient trust may result in disuse, regardless of the robot’s capabilities, whereas excessive trust can lead to safety issues. While most studies of trust in HRI are based on questionnaires, in this work it is explored how participants’ trust levels can be recognized based on electroencephalogram (EEG) signals. A social scenario was developed where the participants played a guessing game with a robot. Data collection was carried out with subsequent statistical analysis and selection of features as input for different machine learning models. Based on the highest achieved accuracy of 72.64%, the findings indicate the existence of a correlation between trust levels and the EEG data, thus offering a promising avenue for real-time trust assessment during interactions, reducing the reliance on retrospective questionnaires.