Can a robot elicit emotions? A Global Optimization Model to attribute mental states to human users in HRI∗

In this work, we are interested in investigating if a distinct personality of the robot may impact the emotional state of the users, which we propose to detect using neuroscience theories that allow us to classify emotions based on valence and arousal metrics derived from brain wave activity analysis. We devised an experimental research study in which EEG data was gathered while individuals interacted with a robot with different personalities. Support Vector Machine, Decision Tree, Random Forest, K-Nearest Neighbors, and Multi-Layer Perceptrons have all been trained using EEG-signal, valence, and arousal data. All proposed classifiers were subjected to a Global optimization Model (GOM) that used feature selection and hyper-parameter optimization techniques to improve classification results and address common issues that affect classifier accuracy when attempting to solve a supervised learning problem, such as bias-variance trade-off, dimensionality of the input space, and noise in the input data space. The findings of the experiments will be presented and debated.