Collaboration between humans and robots is becoming increasingly crucial in our daily life. In order to accomplish efficient cooperation, trust recognition is vital, empowering robots to predict human behaviors and make trust-aware decisions. Consequently, there is an urgent need for a generalized approach to recognize human-robot trust. This study addresses this need by introducing an EEG-based method for trust recognition during human-robot cooperation. A human-robot cooperation game scenario is used to stimulate various human trust levels when working with robots. To enhance recognition performance, the study proposes an EEG Vision Transformer model coupled with a 3-D spatial representation to capture the spatial information of EEG, taking into account the topological relationship among electrodes. To validate this approach, a public EEG-based human trust dataset called EEGTrust is constructed. Experimental results indicate the effectiveness of the proposed approach, achieving an accuracy of 74.99% in slice-wise cross-validation and 62.00% in trial-wise cross-validation. This outperforms baseline models in both recognition accuracy and generalization. Furthermore, an ablation study demonstrates a significant improvement in trust recognition performance of the spatial representation. The source code and EEGTrust dataset are available at https://github.com/CaiyueXu/EEGTrust.

翻译：人机协作正日益成为日常生活中的关键环节。为实现高效协作，信任识别至关重要，它使机器人能够预测人类行为并做出基于信任的决策。因此，迫切需要一种通用方法来识别人与机器人之间的信任关系。本研究通过引入一种基于脑电图（EEG）的人机协作信任识别方法，回应了这一需求。研究采用人机协作游戏场景，模拟人类与机器人协作时的不同信任水平。为提升识别性能，本研究提出了一种结合三维空间表征的EEG视觉Transformer模型，通过考虑电极间的拓扑关系来捕获EEG的空间信息。为验证该方法的有效性，研究构建了名为EEGTrust的公开脑电人类信任数据集。实验结果表明了该方法的有效性：在切片式交叉验证中准确率达到74.99%，在试次式交叉验证中达到62.00%，在识别准确率和泛化能力上均优于基线模型。此外，消融实验证明空间表征显著提升了信任识别性能。源代码及EEGTrust数据集已开源至https://github.com/CaiyueXu/EEGTrust。