On Making a Decision to Correct the Learner Behavior Based on the Signals of their Electroencephalograms When Organizing Interaction with an Anthropomorphic Teacher-Assistant Robot

Abstract

The work discusses the method of recognizing the state of a learner using artificial neural networks based on the analysis of electroencephalogram signals during the assimilation of educational material using a hardware software complex of a robot – an assistant teacher of anthropomorphic type.