Brain-robot interaction (BRI) empowers individuals to control (semi-)automated machines through their brain activity, either passively or actively. In the past decade, BRI systems have achieved remarkable success, predominantly harnessing electroencephalogram (EEG) signals as the central component. This paper offers an up-to-date and exhaustive examination of 87 curated studies published during the last five years (2018-2023), focusing on identifying the research landscape of EEG-based BRI systems. This review aims to consolidate and underscore methodologies, interaction modes, application contexts, system evaluation, existing challenges, and potential avenues for future investigations in this domain. Based on our analysis, we present a BRI system model with three entities: Brain, Robot, and Interaction, depicting the internal relationships of a BRI system. We especially investigate the essence and principles on interaction modes between human brains and robots, a domain that has not yet been identified anywhere. We then discuss these entities with different dimensions encompassed. Within this model, we scrutinize and classify current research, reveal insights, specify challenges, and provide recommendations for future research trajectories in this field. Meanwhile, we envision our findings offer a design space for future human-robot interaction (HRI) research, informing the creation of efficient BRI frameworks.

翻译：脑机交互（BRI）使个体能够通过大脑活动（被动或主动）控制（半）自动化机器。过去十年间，脑机交互系统取得了显著成就，主要采用脑电图（EEG）信号作为核心组件。本文对近五年（2018-2023年）发表的87项精选研究进行了最新且详尽的审视，重点勾勒了基于脑电的脑机交互系统的研究图景。本综述旨在整合并阐明该领域的研究方法、交互模式、应用场景、系统评估、现存挑战及未来研究潜在方向。基于分析，我们提出了包含“大脑”、“机器人”与“交互”三要素的脑机交互系统模型，揭示了脑机交互系统的内在关系。我们特别探究了人类大脑与机器人之间交互模式的本质与原理——这一领域尚未被学界明确界定。随后，我们从不同维度探讨了这些要素。在该模型框架下，我们审视并分类了当前研究，揭示了洞见，明确了挑战，并为该领域未来研究轨迹提供了建议。同时，我们期望研究能为未来人机交互（HRI）研究提供设计空间，助力构建高效的脑机交互框架。