  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.
