Affective recognition using EEG signal in human-robot interaction

Abstract

Human-robot interaction is a crucial field in human factor field and mechanical arm operation is a widely used form in human-robot interaction. However, the mistaken operations caused by the affect influction of operators are still one of the dominant reasons causing accidents. Because of the close link between affective state and human error, in this paper, we analyzed the EEG signal of five subjects operating mechanical arm and the track record of the mechanical arm movement. A combination label model including the subjective part and the objective part are proposed to reflect the real time affective state influction. Additionally, in subsequent recognition experiment, the results indicate that the affect is a state of mind that requires a relatively longer period of time to be effectively represented and the frequency domain features are significantly more important than time domain features in affective recognition process using EEG signal.