Abstract
Brain-computer interfaces (BCIs) have long been seen as control interfaces that translate changes in brain activity, produced either by means of a volitional modulation or in response to an external stimulation. However, recent trends in the BCI and neurofeedback research highlight passive monitoring of a user's brain activity in order to estimate cognitive load, attention level, perceived errors and emotions. Extraction of such higher order information from brain signals is seen as a gateway for facilitation of interaction between humans and intelligent systems. Particularly in the field of robotics, passive BCIs provide a promising channel for prediction of user's cognitive and affective state for development of a user-adaptive interaction. In this paper, we first illustrate the state of the art in passive BCI technology and then provide examples of BCI employment in human-robot interaction (HRI). We finally discuss the prospects and challenges in integration of passive BCIs in socially demanding HRI settings. This work intends to inform HRI community of the opportunities offered by passive BCI systems for enhancement of human-robot interaction while recognizing potential pitfalls.
Highlights
The idea of having intelligent machines that can read people’s minds and react without direct communication had captured human’s imagination
In section Brain-computer interfaces (BCIs) and Human-Robot Interaction, we focus on passive BCI-robot studies that used cognitive and affective state measures as a neurofeedback input for a social or mechanical robot, thereby optimizing their response and behavior in a closed-loop interaction
We describe the current state of the art in each of these domains, laying out a foundation for future employment of passive BCIs in human-robot interaction
Summary
The idea of having intelligent machines that can read people’s minds and react without direct communication had captured human’s imagination. A passive BCI system that extracts this information in real-time can be used in development of hybrid and adaptive systems that optimize the performance of the user either by removing the erroneous trials (Ferrez and Millán, 2008; Schmidt et al, 2012; Yousefi et al, 2019), or by modifying the classification parameters through online learning of the BCI classifier (Krol and Zander, 2017; Mousavi and de Sa, 2019), or by adjusting the task difficulty level to different individuals in order to improve engagement and motivation (Mattout et al, 2015).
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