  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.
