Collaborative brain-computer interface (cBCI) that conduct motor imagery (MI) among multiple users has the potential not only to improve overall BCI performance by integrating information from multiple users, but also to leverage individuals' performance in decision-making or control. However, existed research mostly focused on the brain signals changes through a single user, not noticing the possible interaction between users during the collaboration. In this work, we utilized cBCI and designed a cooperative four-classes MI task to train the dyad. A humanoid robot would stimulate the dyad to conduct both left/right hand and tongue/foot MI. Single user was asked to conduct single MI task before and after the cooperative MI task. The experiment results showed that our training could activate better performance (e.g., high quality of EEG /MI classification accuracy) for the single user than single MI task, and the single user also obtained better single MI performance after cooperative MI training.
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