An EEG Benchmark Data Set for Data-Driven Trust Assessment in Social HRI

Trust has gained popularity for evaluating human robot interaction because trust plays an important role for ensuring successful interactions. Only few studies have so far tackled the challenge of assessing trust during the interaction and thus making it possible to use trust as a control parameter for the robot. To this end, we need to identify relevant factors influencing trust and user behavior and then collect data sets that allow for developing machine learning models for trust assessment. In this paper, we present such data set with EEG data that has been collected from 21 participants in a social human robot interaction. Due to the rigorous approach for data collection presented in this paper, the data set is useful for the community to develop and evaluate trust assessment algorithms.