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.

翻译：在多用户间进行运动想象的协作脑机接口不仅具有通过整合多用户信息以提升整体脑机接口性能的潜力，还能在决策或控制中利用个体的表现。然而，现有研究大多关注单一用户脑信号的变化，未能注意到协作过程中用户间可能存在的交互作用。在本工作中，我们利用协作脑机接口，设计了一项协同四分类运动想象任务来训练二人组。一个人形机器人会刺激二人组同时进行左手/右手以及舌头/足部的运动想象。我们要求单一用户在协同运动想象任务前后分别执行单一运动想象任务。实验结果表明，与单一运动想象任务相比，我们的训练能够为单一用户激发出更优的表现（例如更高的脑电图质量/运动想象分类准确率），并且单一用户在经过协同运动想象训练后也获得了更好的单一运动想象表现。