Detecting the Intention of Object Handover in Human-Robot Collaborations: An EEG Study

Human-robot collaboration (HRC) relies on smooth and safe interactions. In this paper, we focus on the human-to-robot handover scenario, where the robot acts as a taker. We investigate the feasibility of detecting the intention of a human-to-robot handover action through the analysis of electroencephalogram (EEG) signals. Our study confirms that temporal patterns in EEG signals provide information about motor planning and can be leveraged to predict the likelihood of an individual executing a motor task with an average accuracy of 94.7%. We also suggest the effectiveness of the time-frequency features of EEG signals in the final second prior to the movement for distinguishing between handover action and other actions. Furthermore, we classify human intentions for different tasks based on time-frequency representations of pre-movement EEG signals and achieve an average accuracy of 63.5% for contrasting every two tasks against each other. The result encourages the possibility of using EEG signals to detect human handover intention in HRC tasks.